Republic of Poland
Selected Issues

This Selected Issues Paper focuses on the economic and financial ties between Poland and the euro area and analyzes the associated spillovers. It documents stylized facts about trade, vertical integration, foreign direct investment, and banking system linkages between Poland and core euro area countries. The impact of shocks originating from the euro area on economic developments in Poland is quantified using two methods, namely a vector auto-regression model and a small-open-economy quarterly projection model.

Abstract

This Selected Issues Paper focuses on the economic and financial ties between Poland and the euro area and analyzes the associated spillovers. It documents stylized facts about trade, vertical integration, foreign direct investment, and banking system linkages between Poland and core euro area countries. The impact of shocks originating from the euro area on economic developments in Poland is quantified using two methods, namely a vector auto-regression model and a small-open-economy quarterly projection model.

I. Economic and Financial Linkages with the Euro Area1

A. Introduction

1. A subject of much recent academic and policy debate is whether emerging market economies have been able to “decouple” from business cycles in advanced economies, and there has been some empirical evidence supporting the decoupling hypothesis.2 However, the financial crisis of 2008–09 told a different, more gloomy story. In particular, in 2009, real GDP level contracted by 6.1 percent in advanced economies, and by 5.4 percent in emerging and developing economies–almost a one-for-one response. The recent re-escalation of financial and sovereign stress in the Euro area has thus renewed concerns about potential spillovers to emerging Europe, including Poland.

2. This paper takes stock of the economic and financial ties between Poland and the Euro area and analyzes the associated spillovers. We are particularly interested in measuring the potential impacts of a Euro area shock (real or financial) on Poland’s economy. We also investigate the possible channels through which shocks are transmitted across borders.

3. Business cycles in Poland and the Euro area have become increasingly synchronized, as shown by several simple measures based on moments of output growth. For example, the correlation of year-over-year real GDP growth rates between Poland and the Euro area is 0.52 for the entire 1995–2011 sample and 0.81 for post-2004. De-trending the output series using either the univariate (band-pass) or multivariate filters3 does not change the main message. Moreover, the Euro area’s cycle tends to lead that in Poland by one to two quarters in the post-2004 period, suggesting an important role of external factors in driving Poland’s business cycle fluctuations.

A01ufig01

Output Growth Co-Movement

(YoY percent changes)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Sources: Eurostat

Poland: Business Cycle Synchronization with Euro Area

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4. The observed increase in business cycle synchronization can be explained by the changing nature of shocks or shock transmission, or both. In particular, it is possible that “global” shocks (i.e. shocks common to both countries) have become more volatile, or that country-specific shocks have become more highly correlated. It is also possible that since countries have become more integrated in the goods, capital, and financial markets, country-specific shocks are now more easily transmitted across borders.4 Properly disentangling these effects is a difficult task, and beyond the scope of this analysis. We merely conjecture that the greater trade and financial integration between Poland and countries in the Euro area plays a role in further synchronizing the business cycles.5

5. The paper is organized as follows. Section B documents stylized facts about trade, vertical integration, foreign direct investment, and banking system linkages between Poland and the core Euro area countries. The subsequent sections attempt to quantify the impact of shocks originating from the Euro area on economic developments in Poland using two methods, namely a vector auto-regression model (Section C) and a small-open-economy quarterly projection model (Section D). Section E contains a few concluding remarks.

B. Trade and Financial Linkages

6. Although Poland’s economy is less dependent on trade compared to its CEE neighbors6, trade linkages with the Euro area are significant. The value of bilateral trade (exports plus imports) with the Euro area reached over 40 percent of GDP in late 2011, more than double that from fifteen years earlier. Close to 60 percent of Poland’s exports go to Euro area countries, half of which are to Germany alone. Other major trading partners include France, Italy, and the UK, although trade with the Netherlands is growing rapidly due to the strong presence of Dutch firms with direct investment in Poland. Recently, an increasingly larger fraction of Polish exports are shifting eastwards to CEE and CIS countries, with especially strong growth in exports to Russia. The geographical structure of imports is broadly similar to that of exports.

A01ufig02

Bilateral Trade Shares

(Percent of GDP)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Sources: Direction of Trade Statistics; and IMF staff estimates.

Poland: Trade by Partner, 2010

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7. The extensive vertical integration between Germany and CEE countries is boosting trade in Poland.7 Vertical integration between Germany and CEE countries primarily involves production of transport equipment, particularly automobiles, with Germany generally taking a more upstream position and supplying more intermediate inputs. One measure of vertical integration is the size of trade in intermediate and capital goods, which shows the extent to which production processes are geographically fragmented. Trade in intermediate and capital goods accounted for roughly half of total trade between Poland and Germany in 20108; however, this share has remained roughly constant over the 2000–10 period. An alternative measure of vertical integration is the import content of exports. For example, the share of foreign value added in Poland’s gross exports of durable goods increased from 19 percent in 1995 to almost 43 percent in 2005.9 Close to 30 percent of this foreign value added in 2005 originated from Germany. Automobiles account for 13 percent of Poland’s trade with Germany, of which 80 percent is parts and components, and the remaining 20 percent is final vehicles.

A01ufig03

Trade with Germany by Product Group

(Billion US dollars)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Sources: UN Comtrade database; and IMF staff estimates.
A01ufig04

Value Added Contents of Durable Goods Gross Exports

(Million US dollars)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Sources: OECD input-output tables; and IMF staff estimates.

8. Vertical integration may be an important channel transmitting business cycle shocks across borders. For example, using cross-country industry-level data, di Giovanni and Levchenko (2009) find that vertical specialization accounts for roughly 30 percent of the total impact of bilateral trade on business cycle correlation. Bems and others (2010) use a global input-output framework and calculate that during the Great Recession, for every percentage point drop in Western Europe’s output growth caused by declining final demand, growth in emerging Europe dropped by 0.35 percentage points.

9. Substantial foreign direct investment (FDI) further fosters trade links. For Poland, the share of FDI in GDP reached over 40 percent in 2010, twice that in 2000. Three quarters of this FDI are accounted for by investors from the Euro area. Dutch, German, and French firms dominate inward investment activity, whereas Polish firms maintain sizable direct investment position in Italy and Luxembourg, most likely in financial services. Across the CEE, the industry composition of inward FDI varies substantially. While a relatively large fraction of FDI in Poland and the Czech Republic go to the manufacturing sector, which is arguably harder to reverse, most FDI in Hungary is channeled into financial intermediation and real estate. Consequently, while Poland and the Czech Republic both experienced a significant reduction of FDI inflows during the 200 8–09 crisis, unlike Hungary there was no outflow.

A01ufig05

Inward FDI Position by Partner

(Percent of GDP)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Sources: OECD Stat; and IMF staff estimates.
A01ufig06

Industry Composition of FDI Position, 2009

(Percent of total)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Sources: OECD Stat; and IMF staff estimates.

Poland: Foreign Direct Investment Position by Partner, 2010

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10. The banking systems of Poland and the Euro area have also become increasingly integrated. Exposure to BIS-reporting banks, counting both cross-border lending and locally-funded assets of foreign bank subsidiaries, reached almost 60 percent of GDP in late 2011. More than 80 percent of foreign claims are accounted for by Euro area banks, with German, Italian, and Dutch banks in the lead. Exposure to banks in IMF-program countries (Greece, Ireland, and Portugal) is limited, amounting to less than five percent of GDP in 2011. The majority of foreign bank loans (direct and subsidiary lending) are extended to the private non-bank sector. Bank loans represent an important source of finance for Polish enterprises, accounting for about a quarter of total enterprise sector liabilities.10

A01ufig07

Consolidated Claims of BIS-Reporting Banks by Bank Nationality

(Percent of GDP)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Sources: BIS; and IMF staff estimates.

Poland: Consolidated Claims of BIS-Reporting Banks

(Amount outstanding as of December 2011)

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11. In summary, Poland is increasingly integrated with the core Euro area countries through both trade and financial channels. Poland holds a key position in the German supply chain, and is a recipient of substantial and diverse FDI from the core Euro area countries. Both factors contribute to the significant size of bilateral trade, making Poland vulnerable to a negative growth shock in the Euro area through the external demand channel. Further, the sizable foreign ownership of Poland’s banking system also increases the risks that a financial shock in the Euro area will be transmitted to Poland’s financial sector and consequently to the real economy.

12. The following sections offer two approaches to analyze spillovers from the Euro area to Poland. The first attempts to quantify the size of growth spillovers and the relative contribution of various transmission channels (trade, financial, commodity prices) using a simple vector auto-regression (VAR) framework.11 The second approach simulates the effects of a financial shock in the Euro area on the domestic economy using the Global Projection Model (GPM).

C. Vector Auto-Regression

13. We estimate a VAR model that contains quarterly real GDP growth for Poland, the Euro area, and the rest of the world (ROW) for the period 1997:Q2–2011:Q3.12 To identify the impulse responses to a country-specific shock, Cholesky decomposition is used to orthogonalize the errors across individual VAR equations. It is well-known that the results obtained this way are sensitive to the ordering of variables in the decomposition, which assumes the region/country from which disturbances originate. As an attempt to mitigate this problem, we follow Bayoumi and Swiston (2007) and take the average of “plausible” Cholesky orderings – a quasi-Bayesian approach that essentially assigns priors to the direction of causality. Specifically, we assume that shocks originate from the Euro area with probability one-half and from the rest of the world with probability one-half. Since Poland is a small economy compared to the other two regions, any contemporaneous correlation between Poland’s residuals and those of the major regions is assumed to be driven by the larger economies.13

14. Examination of the VAR residuals shows that external shocks have become relatively more important than domestic shocks in driving the volatility of Poland’s economy. Domestic shocks have become less volatile, as indicated by a decline in the estimated standard deviation from 2.5 for the entire sample to 1.84 for the post-2004 sample. Meanwhile, the correlation with Euro area’s shocks has increased (0.35 in full sample vs. 0.49 in post-2004 sample). Lower domestic shock volatility and higher correlation with external shocks offset each other, keeping the covariance between Poland’s shocks and Euro area’s shocks relatively stable over time.

Variances, Correlations, and Covariances of VAR Residuals

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15. Impulse response results show that spillovers from a growth shock in the Euro area to Poland’s economy can be sizable. A typical shock to Euro area’s growth is estimated at 1.25 percentage points on impact, rising to around 4 percentage points after two years. In response, Poland’s growth increases by 0.78 percentage points initially, accumulating to a peak of just over one percentage point after four quarters. Thus, the cumulative response of Poland’s growth to a Euro area shock is 60 percent of the original shock on impact, averaging 30 percent over two years.

A01ufig08

Growth Spillovers from Euro Area to Poland

(Accumulated response of GDP, percent)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Source: Staff estimates.

16. We estimate the contribution of three potential transmission channels, namely trade, financial, and commodity prices, to the overall growth spillovers. The three-variable VAR above is augmented by adding each of the channels as exogenous variables in separate estimations.14 The impulse response given by a VAR augmented with trade variables, for example, can be thought of as measuring the spillovers through all channels other than trade. Thus, we can calculate the contribution of a particular channel as the difference in response between the augmented VAR and the original VAR:

ci,j=riri,j

where ci, j denotes the contribution of channel j in period i, ri is the response from the original VAR, and ri, j is the response from the VAR augmented with channel j.

17. Contributions from the trade and financial channels are estimated to be the most significant, while commodity prices play a limited role. Financial conditions in the Euro area explain a major part of the overall growth spillovers to Poland, especially during the first two quarters immediately after shock. While trade variables are relatively less important for the cross-country transmission of shocks during the first few quarters, contribution of the trade channel increases over time. The sum of contributions from the individual channels is not constrained to equal the estimated overall response from the original VAR, and thus could be used as an alternative estimate of the size of growth spillovers.15

A01ufig09

Decomposition of Spillover Channels

(Percent)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Source: Staff estimates.

18. In summary, the simple VAR exercise produces two main findings. First, growth spillovers from the Euro area to Poland are non-trivial, with an estimated growth elasticity of 0.6 on impact and 0.3 on average over two years. In terms of magnitude, this is however significantly lower than the estimate for entire Central Europe (almost one-for-one)16, possibly due to the relative closedness of Poland’s economy. Second, shocks are transmitted from the Euro area to Poland primarily through trade and financial channels, with financial factors playing the dominant role in the period immediately after shock.

D. Global Projection Model

19. We develop a small quarterly projection model of Poland and the Euro area, which features real-financial linkages as well as financial integration between the two economies. This is a parsimonious variant of the Fund’s Global Projection Model developed in a series of papers17 and is designed to focus primarily on the potential contagion from the Euro area to Poland given the extensive trade and financial linkages documented above. The model is characterized by a few core forward-looking behavioral equations that jointly determine key macroeconomic variables, namely output, unemployment, inflation, short-term interest rate, and the exchange rate. Model parameters are chosen based on a combination of theory, historical data, expert judgment, and estimated parameters from the GPM618.

20. A central feature of our model is the incorporation of an external finance premium (XFP). This premium is defined as the spread between the risk-free rate and the lending rate for nonfinancial firms and households, reflecting the cost of financial intermediation faced by profit-maximizing banks.19 This spread is observed to be counter-cyclical, reflecting developments in credit supply conditions, and seeks to capture a channel of real-financial linkage and cross-country contagion that goes beyond the traditional channels of interest rate and exchange rate.

A01ufig10

External Finance Premium (%)

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

A01ufig11

Euro Area: Output Gap and Premium

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

21. The external finance premium played an important role in the Great Recession, when much of the shock was financial in nature. While the majority of modern recessions prior to 2008–09 were the result of monetary policy tightening to bring down inflation, which was then transmitted to the real economy via the interest rate channel, the onset of the 2008–09 crisis was marked by a sharp and extremely persistent increase in XFP. In particular, the premium in the Euro area increased by 120bps between 2008:Q3 and Q4, and in Poland it increased by roughly 60bps between 2008:Q4 and 2009:Q1. The higher premium initially reflected a drying up of liquidity in the interbank market and the squeeze in credit supply as a result of bank deleveraging, and subsequently elevated default risks of households and firms as the economic outlook weakened. The figure shows that XFP in the Euro area and Poland are highly correlated, in part due to the significant presence of Euro area’s banks in the Polish banking system20, and that a rise in premium is a potential indicator of subsequent economic downturn. The cross-correlation structure of the two premium series serves as a basis for our choice of the magnitude of cross-country financial spillovers in the model.

22. We study the impulse responses of key variables to a scenario of intensified financial stress in the Euro area (Figure 1). In particular, we assume that, under an adverse financial shock, the Euro area’s external finance premium increases by 100bps for one quarter. Due to the spillovers, Poland’s premium quickly increases in response, peaking at 25bps three quarters after the original shock. As a result, the output gap drops by 0.3 percentage point in the Euro area and 0.2 percentage point in Poland. The impact of the financial shock on the domestic economy is persistent: the accumulated output loss relative to baseline (no shock) amounts to 1.5 percentage points over three years. While the negative effect on output in the Euro area results exclusively from the higher financing cost, Poland’s output is affected both directly via premium shock spillovers and indirectly via weak foreign demand. Depressed domestic demand reduces inflationary pressures despite some offsetting pass-through effect from a depreciation of the exchange rate. Assuming monetary policy is operating under no constraints, central banks in both economies respond by cutting the short-term interest rate to boost the economy. The model predicts that the NBP would have reduced the policy rate by a cumulative 280 basis points by the end of year three.

Figure 1.
Figure 1.

Responses to Euro Area Premium Shock

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Source: GPM simulation.

23. We now examine a scenario in which the two economies are faced with the same premium shock but monetary policy reaction is delayed (Figure 2). In particular, in the first scenario (blue dash lines), the European Central Bank is constrained by the binding zero-lower-bound and thus cannot lower the nominal interest rate for four quarters. In the second scenario (red dash-dot lines), Poland’s central bank delays its reaction by one quarter, for example due to concerns over the impact of rising global risk aversion on the exchange rate (and therefore inflation, given pass-through). In each case, agents in the model are assumed to fully anticipate the central bank’s inaction and act accordingly. The simulation shows that under NBP inaction even for just one quarter, the domestic economy suffers from a significantly deeper downturn compared to the scenario with instant policy reaction (black lines). The effects are much less severe when the policy constraint is on the part of the ECB. This is because, although Poland would be affected through the external demand channel, the immediate reduction in the domestic interest rate brings about a large exchange rate depreciation, mitigating the negative output response by supporting net exports.21 The experiment highlights the cushioning role of monetary policy in Poland in counteracting adverse external shocks.

Figure 2.
Figure 2.

Responses to Euro Area Premium Shock with Anticipated Monetary Policy Delay

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Source: GPM simulation.

Key Model Equations

The domestic economy is characterized by five core behavioral equations that jointly determine the output gap, inflation, short-term nominal interest rate, exchange rate, and unemployment gap22.

• IS curve (aggregate demand)

yt=β1yt1+β2yt+1β3rtβ4(μtμss)+β5zt+β6ytEU+ϵty

This equation relates the output gap (yt) to its own lead and lagged values, the real interest rate gap (γt), the deviation of the external finance premium from its steady-state value (μt - μss), the real exchange rate gap (zt), and the output gap in the Euro area (ytEU) which captures the direct trade link between the two economies. XFP follows an autoregressive process with direct spillovers from the premium in the Euro area, and the strength of spillovers is governed by the parameter c_spill:

μt=ρμμt1+(1ρμ)[μss+c_spill(μtEUμssEU)]+ϵtμ

• Phillips curve (inflation)

πt=πtc+ϵtπ
πtc=λ1πt+1c+(1-λ1)πt-1c+λ2yt+λ3zt+εtπc

Headline inflation (πt) is modeled as the sum of a measure of core inflation (πtc) and an i.i.d shock capturing high-frequency dynamics (ϵtπ). Core inflation is a function of both its past and future values, the output gap, the real exchange rate gap, and a mark-up shock (ϵtπc).

• Monetary policy rule

it=γ1it1+(1γ1)[r¯t+πt+1tar+γ2(π4t+4πt+4tar)+γ3yt]+ϵti

The short-term nominal interest rate (it) is determined by a variant of the Taylor’s rule. The central bank aims at achieving a measure of the equilibrium nominal interest rate over the long run (r¯t+πt+1tar), while responding to deviations of the expected year-on-year inflation from the inflation target (π4t+4πt+4tar) and to the current output gap, as well as smoothing interest rate movements (lagged term).

• Uncovered interest parity (UIP)

ititEU=4(Est+1st)+ρ¯t+ϵtuip

The UIP equation relates the interest rate differential between the two countries to the expected change in the nominal exchange rate (Est+1 - st) and an equilibrium risk premium on zloty-denominated assets (ρ¯t). The expected nominal exchange rate (Est+1) is defined as a weighted average of model-consistent solution of the exchange rate 1 period ahead (st+1) and a backward-looking estimate of Est+1, where Δs¯t denotes the annualized quarter-on-quarter change in the equilibrium exchange rate.

Est+1=ϕst+1+(1ϕ)(st1+2Δs¯t/4)

• Dynamic Okun’s law

ut=α1ut1+α2yt+ϵtu

The unemployment gap (ut) is a function of its lagged value and the contemporaneous output gap.

The “foreign block” of the model contains the following main equations for the Euro area:

ytEU=β˜1yt1EU+β˜2yt+1EUβ˜3rtEUβ˜4(μtEUμssEU)+ϵ˜ty
μtEU=ρ˜μμt1EU+(1ρ˜μ)μssEU+ϵ˜ty
πtEU=πtc,EU+ϵ˜tπ
πtc,EU=λ˜1πt+1c,EU+(1λ˜1)πt1c,EU+λ˜2ytEU+ϵ˜tπc
itEU=γ˜1it1EU+(1γ˜1)[rtEU+πt+1tar,EU+γ˜2(π4t+3EUπt+3tar,EU)+γ˜3ytEU]+ϵ˜ti

24. Underlying this exercise is a crucial assumption about the persistence of the premium shock in the Euro area. In particular, if households and firms expect financing costs to remain elevated for a long period of time, the cut back in consumption and investment would likely be more substantial. The shock persistence is primarily governed by the autocorrelation parameter of the premium process for the Euro area (ρ˜μ). In Figure 3, we study the effects of a higher persistence parameter, i.e. ρ˜μ=0.8 compared to the baseline value of 0.7. As expected, a more persistent increase in Euro area’s finance premium is associated with a larger reduction in the domestic output gap and inflation, and in response Poland’s central bank reduces the policy rate more aggressively by roughly 20bps relative to the baseline. Again, both trade and financial channels play a role. The financial consequence would be more severe in a plausible scenario where, faced with a more persistent shock to the Euro area’s premium, Polish consumers perceive that the resulting increase in domestic premium would also be more persistent (higher ρμ).

Figure 3.
Figure 3.

Responses to a More Persistent Euro Area Premium Shock

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Source: GPM simulation.

25. Unlike persistence of the premium shock, the model results are relatively insensitive to changes in the magnitude of the spillover parameter (Figure 4). In particular, in the baseline we choose the spillover parameter c_spill= 0.76 to reflect the estimated cross-correlation between Poland’s and the Euro area’s historical premium. In the scenario with stronger spillovers, e.g. due to a more closely integrated banking system than in the past, we let c_spill = 0.95. Compared to the previous scenario of higher shock persistence, the responses of domestic output, inflation, interest rate and exchange rate are relatively insensitive to changes in the spillover parameter.

Figure 4.
Figure 4.

Responses to Euro Area Premium Shock with Stronger Spillover

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

Source: GPM simulation.

26. We proceed to confront the model with observed data and examine how the model interprets history. Specifically, using historical data on output, inflation, unemployment, exchange rate and the policy rate, the parameterized model can be used to estimate the various structural shocks as well as the unobservable quantities such as the output gap and the equilibrium exchange rate. We then decompose the estimated output gap series for Poland over the period 2004:Q1–2011 :Q4 into relative contributions of the structural shocks.23 This exercise allows us to evaluate, in the model’s eyes, what are the important sources of shocks driving Poland’s economy during the past eight years.

A01ufig12

Historical Shock Decomposition

Citation: IMF Staff Country Reports 2012, 163; 10.5089/9781475506549.002.A001

27. The historical shock decomposition shows that shocks from the Euro area play a major role in driving Poland’s business cycle fluctuations, particularly in the recent period. In particular, the contrast between the 2005–06 downturn and the past financial crisis is remarkable. During the 2005–06 period, domestic policy shocks were the main driver of the output gap, and there was very little contribution from either demand or financial factors. On the other hand, the 2008–09 downturn was driven primarily by adverse shocks to domestic and foreign demand as well as by premium shocks in both countries, with Euro area shocks accounting for roughly half of the total negative contributions. While the subsequent recovery was supported by a rebound in domestic demand, external factors including the persistently high Euro area finance premium continued to weigh on the recovery. The decomposition also shows that Poland’s monetary policy stance was supportive to growth during and in the aftermath of the crisis; the NBP indeed reduced the policy rate by a cumulative 250bps between 2008:Q3 and 2009:Q3. Finally, while premium shocks tend to be pro-cyclical, exchange rate movements were instrumental in smoothing out Poland’s cyclical fluctuations.

E. Concluding Remarks

28. Poland is increasingly integrated with the Euro area through trade, vertical integration, FDI, and banking channels, giving rise to strong (positive and negative) spillovers. Using a simple VAR framework, we estimate that growth spillovers from the Euro area to Poland can be sizable with an elasticity of 0.6 on impact, and that growth shocks are transmitted through primarily trade and financial channels. Incorporating cross-country financial linkages into the Global Projection Model allows us to study the endogenous responses of the output gap, inflation, exchange rate, and particularly monetary policy to an external financial shock. The historical shock decomposition confirms that real and financial shocks from the Euro area have become a major driver of Poland’s business cycle fluctuations.

29. It is analytically challenging to measure spillovers across countries and determine the contributing factors. The methods illustrated here, while promising, are subject to several caveats. Among the most criticized issues with non-structural methods like VARs are how to properly identify the effects of country-specific shocks (i.e. stripped of common shocks) and how to condition the responses on a set of initial conditions (e.g. spillovers may be asymmetric between upturns and downturns). The Global Projection Model provides a consistent approach to forecasting, policy analysis and risk assessment, with appropriate consideration of relevant foreign developments and the potential spillover channels. It is also sufficiently simple to allow effective communication of policy results, an advantage that large micro-founded models do not possess. However, the price to be paid for simple intuition is the omission of important aspects such as fiscal policy, and the treatment of financial sector remains at a rudimentary stage. It is hoped that as the sophistication of analytical tools improves, future analysis of cross-country spillovers will also benefit.

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1

Prepared by Giang Ho (EUR). Roberto Garcia-Saltos and Michal Andrle (both RES) provide inputs on the Global Projection Model.

3

The multivariate filter uses data on inflation, unemployment, and capacity utilization to measure potential output. See Benes and others (2010) for more detail.

4

See Frankel and Rose (1998), Baxter and Crucini (1995) for some early evidence on the role of trade and financial factors in driving international business cycle co-movements.

5

From a theoretical perspective, however, the correlation between business cycle synchronization and integration is not necessarily positive. Krugman (1993) noted that stronger trade integration may lead to greater regional specialization, which can lead to less output synchronization with industry-specific shocks.

6

Poland has an exports-to-GDP ratio of 40 percent, compared to an average of 65 percent for new EU member states.

7

See Hummels, Ishii and Yi (2001) for evidence on the growth of vertical integration in world trade.

8

The classification of intermediate and capital goods follows HS standard product grouping in UN Comtrade database.

9

See the study by Strategy, Policy and Review Department (2011), which calculate the import content of exports for a number of countries using OECD input-output databases.

10

National Bank of Poland’s “Financial Stability Report,” December 2011.

11

This approach is similar to that developed by Bayoumi and Swiston (2007–08).

12

The ROW aggregate consists of the United States and 12 smaller countries: Australia, Canada, Denmark, New Zealand, Norway, Sweden, Switzerland, United Kingdom, Korea, Mexico, South Africa, and Taiwan. The aggregate growth rate is calculated by weighting each country’s growth rate by its PPP GDP.

13

Although we are not able to satisfactorily control for common or global shocks in this simple framework, we expect the ROW aggregate to pick up some of the effects of global shocks, being an aggregate of countries that are diverse in geography and industrial structure.

14

Thus, the implicit assumption is that the three transmission channels are uncorrelated. Following Bayoumi and Swiston (2007), we use net export contribution to real GDP growth to measure the trade channel, and equity prices, short and long-term interest rates of the two major regions to measure the financial channel. To capture the commodity price channel, we include in the regression the S&P Goldman Sachs Commodity Index.

15

The fact that the individual channels add up to more than the estimated overall response could indicate that the channels as measured here are not independent. For example, if difficult financial conditions in the Euro area translate into a dry-up of trade financing for Polish exporters which in turn affect Poland’s exports, this effect will be captured in the estimates of both the financial and trade channels.

16

See Akinci and Jeasakul (2011), Chapter 4 of the Regional Economic Outlook: Europe. Central Europe comprises the Czech Republic, Hungary, Poland, the Slovak Republic, and Slovenia. The estimated growth elasticity is with respect to Western Europe, which includes the Euro area together with Denmark, Iceland, Norway, Sweden, Switzerland, and the United Kingdom.

18

The GPM6 is a small quarterly model covering six regions: United States, the Euro area, Japan, emerging Asia, the five Latin America inflation-targeting countries, and a remaining countries grouping. The model is estimated using Bayesian methods. See Carabenciov and others (2012) for more detail.

19

In the Costly State Verification (CSV) type of models, as in e.g. Bernanke, Gertler and Gilchrist (1999), Christiano, Motto and Rostagno (2010), this is the cost of overcoming the information asymmetry between the lenders and the borrowers. To compute the empirical spreads, we use the reference rates for Poland and the Euro area as a measure of the risk-free rate. As the lending rate, we use the loan rate for nonfinancial firms (excluding overdraft) for Poland, and the rate on new business loans of 1-to-5-year maturity for the Euro area.

20

Enders and others (2011) provide a micro-founded theory for the international transmission of financial shocks by incorporating a “global bank” into a two-country business cycle model.

21

Given the significant dollarization in Poland’s credit market, exchange rate depreciation may also lead to negative balance sheet effects that counteract the positive external demand channel.

22

“Gap” variables refer to deviation from equilibrium values. Variables denoted with a “bar” refer to equilibrium values.

23

The numerous shocks in the model are grouped into a few broad categories for expositional clarity.

Appendix I. Structural Liquidity

This appendix defines structural liquidity and its components. Structural liquidity refers to the aggregate liquidity position of the banking system, which corresponds to the sum of autonomous factors that are beyond the control of the central bank in the very short run.

The supply of liquidity through autonomous factors can be derived from a simplified balance sheet of the central bank. By netting the external position of the central bank and the position against the government, and summarizing all other assets and liabilities (other items, net), a simplified balance sheet is shown as follows:

A Simplified Balance Sheet of the Central Bank

article image

Accordingly, factors influencing the liquidity supply can be derived as:

article image

Three situations related to structural liquidity are possible. When the structural liquidity exceeds the MRR, the banking system has a structural liquidity surplus with respect to the central bank, meaning that it does not need to obtain funding from the central bank. Instead, the central bank may conduct OMOs to absorb surplus liquidity from the banking system. Poland’s banking system is currently in a structural liquidity surplus position. When the structural liquidity is exceeded by the MRR, the banking system has an aggregate liquidity deficit with respect to the central bank, and lending to banks by the central bank may be needed to relieve the liquidity shortage. The third case is a balanced structural liquidity position, which means structural liquidity is equal to the MRR.

References

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  • Perez Quiros, Gabriel, and Hugo Rodriguez Mendizabal, 2006, “The Daily Market for Funds in Europe,” Journal of Money, Credit and Banking, Vol. 38, No. 1, pp. 91118 (Columbus, Ohio: The Ohio State University).

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1

Prepared by Yinqiu Lu (EUR).

2

There were ten, four, and 22 days of positive spreads in 2009, 2010, and 2011 respectively.

3

Following the empirical model in Panigirtzoglou and others (2000), the spread between the POLONIA and policy rate (st) can be modeled as: stst1=α1+α2st1+ϵt; E(ϵt2)=σt2=β1+β2ϵt12+β3σt12, with the long-run mean = (—α1/α2); the persistence of the variance=β2+β3, where the variance is explosive if the value is greater than one; and, if β2 + β3 < 1, the variance =β11(β2+β3)

4

Structural liquidity= NBP net foreign assets+net credit to government-currency in circulation+other items net. See Appendix I for a more detailed explanation.

5

See the Selected Issues Paper, Economic and Financial Linkages with the Euro Area.

Republic of Poland: Selected Issues
Author: International Monetary Fund