Bank of Lithuania
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No 50
2024-02-15

Anatomy of inflationary shock in Lithuania: causes, effects and implications

  • Abstract

    After a decade of muted consumer price growth, inflation has picked up again, with the price increase of many goods and services spiking in 2022. Two extraordinary events – the COVID-19 pandemic and the Russian aggression against Ukraine – played a leading role in the jump in inflation. The high risk of deep recession in 2020-2021 forced governments and central banks to implement various supportive measures. As the economies adapted to the pandemic, recoveries followed unexpectedly quickly with the help of expansive monetary and fiscal policies. Nevertheless, pandemic-induced supply-chain disruptions have resulted in delivery delays and increased production and transportation costs across the globe. Thus, recovering demand faced a still-constrained supply. In 2021, recovering economies were hit by another shock – the Russian war against Ukraine. The war contributed to a rise in energy prices (notably, that of natural gas, which Europe was especially dependent on). All these factors – expansionary fiscal and monetary policies, rapidly recovering economies, residual post-pandemic disruptions in supply chains, and increases in energy prices – have led to an unexpected rise in inflation throughout the world, including Lithuania.

    In this occasional paper, we analyse various topics related to inflation in Lithuania, predominantly focusing on the recent inflationary episode. The latter rise of inflation was unprecedented. In 2022, average annual inflation reached 18.9 per cent in Lithuania, a level which had not been seen for more than two decades. We analyse the nature of the recent inflation shock, duration, underlying causes, and consequences. While this study mainly deals with Lithuania, it also addresses the question of whether its inflation dynamics differs from that in the rest of the euro area, and if so, how. The study thus contributes to a more nuanced understanding of inflationary process in Lithuania. While integrated within the general topic, each of the chapters in the study can be seen as separate analytical notes focusing on distinct topics.

    In Section 1.1. (“Stylized facts of consumer price dynamics”) and Section 1.2 ("Dynamics of consumer, producer and input prices”), we provide an overview of the dynamics of inflation and its components in Lithuania over the past two decades. During the economic boom of 2004 to 2008, Lithuania experienced an upward pressure in consumer prices. This ended in 2009 with the global financial crisis, which triggered a significant downturn in the Lithuanian economy. Afterwards, a period of relative stability in inflation took place until the COVID-19 pandemic. At its start in 2020, consumer price inflation decelerated, but price growth picked up in 2021-2022. Since reaching its peak in 2022, the annual inflation rate has been steadily declining. Historically, energy prices in Lithuania have been characterized by especially high volatility. During periods of higher inflation, they have been one of the main drivers of inflation, while during periods of lower inflation, energy prices have been an important factor reducing it.

    In Section 1.3. (“Inflation expectations of Lithuanian households”) and Section 1.4. (“Inflation expectations of Lithuanian firms”), we use existing survey data on Lithuanian households’ and firms’ inflation expectations to better understand their evolution in the recent high inflation environment. A clear upward bias can be observed in households’ and firms’ inflation expectations. However, there is also a significant co-movement between actual inflation and inflation expectations. As inflation started to decline in 2023, similar trends can be observed in inflation expectations. 

    In Section 2.1. (“Effects of energy supply shocks on price inflation along the production chain”), we assess the impact of energy supply shocks on price inflation along the production chain in Lithuania. The energy shocks are identified in two independent monthly BVAR models (Messner and Zorner (2023)). Producer price inflation for energy and food reacts at half the rate of equivalent international inflation in the month of the shock and then continues to rise for a year or year and a half. Consumer food price inflation reacts to a similar extent as producer food price inflation, while consumer energy price inflation reacts to a lesser extent than producer energy price inflation. More importantly, these reactions occur with a lag of about one year after the shock. Finally, the impact at the bottom of the production chain, i.e. on core consumer price inflation, is quite limited. Overall, this section shows that energy supply shocks propagate gradually through the supply chain over time and are not passed on, on a one-by-one basis, to the final consumer.

    In Section 2.2. (“Wage and price responses to aggregate and labour market shocks”), we assess how global and labour market shocks affect wages and consumer prices in Lithuania, and how wage responses in turn affect prices in a quarterly BVAR. Aggregate demand, aggregate supply, labour supply and wage markup shock s are identified following Foroni et al. (2018). The impulse response functions (IRFs) show that global macroeconomic shocks have a persistently higher impact on wages (hourly earnings) and consumer prices than labour-specific shocks. Typical price and wage reactions have their maximum effects after about a year, underlining their rigidity to change. Counterfactual scenarios, in which wages do not react to shocks, reveal that such wage-price spirals can be significant after aggregate supply and demand shocks. Following a demand shock, wage reactions fuel price reactions in the medium term. Following a supply shock, wage reactions counterbalance price reactions over time.

    In Section 2.3. (“Energy price inflation shocks in Lithuania and the Euro area”), we analyse how the energy price shocks affect economies in Lithuania and the Euro area. We estimate two separate BVAR models (one for Lithuania, the other for the EA), including respective time series from 2002Q1 to 2022Q4 of yoy energy, food, and core HICP inflation, as well as the unemployment rate and yoy total compensation per employee, following Corsello and Tagliabracci (2023). The IRFs show that Lithuania was more vulnerable to, and more affected by, energy price inflation shocks than the EA on average over the period. For an equivalent energy shock, the effects on HICP consumer price and wage inflation were larger and more persistent.

    In Section 2.4. (“What has driven the surge in inflation in Lithuania? A production-side decomposition.”), using input-output tables, we decompose the inflation into its four drivers – prices of energy, prices of other imported products, wages and gross operating surplus. In our analysis, we focus on the period from 2021Q1 to 2023Q2 and find that all these supply-side factors contributed significantly to the increase in price level. We show that wage increases accounted for 40% of the calculated increase in price level, while the remaining increase was accounted for in broadly similar proportions by higher energy costs, more expensive imports of non-energy goods and services, and an increase in non-energy sector gross operating surplus (profit). The analysis also indicates that the recent increase in production costs has not yet been fully passed on to consumer prices in 2023Q2.

    In Section 2.5. (“Lithuania’s nominal effective exchange rate fluctuations and domestic inflation.”), we analyse whether changes in nominal effective exchange rates have played a significant role in the recent surge of inflation. A relatively large share of Lithuanian imports is denominated in foreign currency, implying that inflation can be at least partially explained by currency depreciation. To determine the exchange rate pass-through to prices, a simple VAR analysis is conducted. The results of analysis indicate that exchange rate pass-through to import prices is incomplete in Lithuania, meaning that there is no tit-for-tat increase in import prices following currency depreciation. The pass-through for producer and consumer prices is even lower. Nominal exchange rate developments explain slightly more than 10% of import price variability, yet only about 1% of producer and consumer price variability. It follows that although the depreciation of the euro contributed to increasing inflation in the most recent inflation period (2021–2022) in Lithuania, its impact on producer and consumer prices was very limited.

    In Section 2.6 (“A comparison of consumption basket item weights and price levels in Lithuania and the euro area”), we analyse if the differences in the composition of consumption baskets in Lithuania and euro area can explain a significant portion of inflation differentials. While gradually converging, the structure of the Lithuanian consumption basket still differs somewhat from that in the EA average. The greatest differences exist in the weights of services and food. In countries with a higher standard of living, households tend to spend less on basic needs and more on services. The same trends are observed in the development of the Lithuanian economy; as the standard of living approaches the EU average, the price level also converges, and services become a more prominent part of the consumption basket. Different weights of various goods and services in consumption baskets lead to different item weights for inflation calculation. Our calculations show that if in Lithuania we had HICP weights equal to those in the EA, our average annual headline inflation rate would have been about 1.6 percentage points lower than the factual in 2022.

    In Section 2.7. (“Can price level convergence explain longer-term differences in inflation rates across euro area countries?”), following Honohan and Lane (2003), we provide evidence that in a monetary union, remaining price level differences lead to higher inflation in countries with lower price levels. For every single percentage point (pp) deviation below the average price level, countries experience around 0.02-0.036 pp higher inflation. In 2022, the price level in Lithuania was still 26 percent below the EU average. This would imply that the annual inflation in Lithuania could be about 0.5-0.9 pp higher than EA average due to the price level convergence in 2022.

    The unexpectedly high inflation has affected government finances substantially. In Section 3 (“Implications of temporary acceleration in inflation for public finances”), we analyse the implications of higher inflation on the fiscal position of the general government sector. We break down recent general government revenue growth into four explanatory factors: real economic activity, price growth, the effect of government’s discretionary decisions (fiscal measures) and the unexplained component or tax residual. Our decompositions show that in 2021-2022, the observed increase in tax revenue was significantly affected by the strong growth of the macroeconomic bases and implemented fiscal measures. As regards the impact of inflation, more than half of the increase in receipts from VAT, personal income tax and social contributions can be attributed to the rise in the price component. As inflation decelerates, there will be a corresponding slowdown in the nominal GDP growth and the deceleration in goods and services inflation. This would naturally slow the growth of general government revenue.

    All in all, this analysis implies that during the periods of a temporary increase in inflation fiscal policy should resist using the inflation-induced proceeds to finance permanent increases in spending. In the near future, inflation could show persistence and respond more slowly to changing trends in import and producer prices (compared to the upswing) since not all of the increased costs were fully passed through to consumer prices by the middle of 2023. In the longer term, inflation prospects in Lithuania will depend not only on economic policy but also future changes in the energy sector and climate-related developments and their impact, as well as the ability to adapt to those shifts.

    Keywords: inflation, convergence, price level, supply shock

    JEL Codes: C25, E61, G18, G21, G51

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 34
2023-12-31

Consumer price rigidity in periods of low and high inflation: the case of Lithuania

  • Abstract

    I provide new monthly statistics on consumer price rigidity in Lithuania. The statistics are derived from CPI price records, covering an average of 90% of the ECOICOP4 weights between 2019 and 2023. Through a comparative study of two distinct periods – low inflation from January 2019 to December 2020, and high inflation from January 2021 to March 2023 – a significant shift in the frequency of price changes is observed in the latter period. This shift is mainly due to a significant rise in the frequency of price increases, while the average size of these increases has remained relatively constant over the years. Furthermore, I show that structural aggregate demand and energy shocks induced shifts in the frequency of price changes during the high-inflation period, suggesting that state-dependent sticky price models may be more suitable than time dependent ones for explaining inflation fluctuations in Lithuania.

    Keywords: consumer price rigidity, price-setting, high inflation, frequency of price changes.

    JEL codes: D40, E31, E50

No 118
2023-12-21

The Dynamics of Product and Labor Market Power: Evidence from Lithuania

  • Abstract

    This paper characterizes the power dynamics of firms in both product and labor markets in Lithuania between 2004 and 2018. We first show that both markets are not perfectly competitive, as both price markups and wage markdowns are far from unitary and homogeneous. Interestingly, we unveil that the Dynamics of these margins followed different patterns. On the one hand, both the dispersijon and the economy-wide markup have increased, indicative of an increase in product market power. On the other hand, we document a decline in monopsony power, as both the heterogeneity and the aggregate level of markdowns have declined. Altogether, our results underline the importance of jointly analyzing product and labor markets when assessing firms’ market power.

    Keywords: Firm heterogeneity, Monopoly, Markups, Monopsony, Markdowns.

    JEL Classification: D4, E2, J3, L1.

No 44
2023-01-13

Wage Growth in Lithuania from 2008 to 2020: Observed Drivers and Underlying Shocks

  • Abstract

    This paper studies the drivers of wage growth in Lithuania over the period 2008-2020. Using administrative data as well as aggregate measures reflecting the state of the economy, we estimate an extended version of a wage Phillips curve. Our reducedform estimates indicate that nominal wage growth was tightly linked to labor market fluctuation over this period. Labor productivity, changes in the minimum wage, and the composition of employment also contributed to wage dynamics. However, we find little evidence that past inflation has been a push factor. To understand the underlying economic primitives behind our findings, we estimate a structural Bayesian autoregressive model. Our structural analysis reveals a significant contribution from aggregate supply shocks, reflecting a stronger relationship between productivity and wages than implied by our reduced-form estimates. Moreover, the historical decomposition reveals that since 2013, wages grew over and above productivity due to rising aggregate demand and labor market disturbances.

No 105
2022-06-20

New Facts on Consumer Price Rigidity in the Euro Area

  • Abstract

    Using CPI micro data for 11 euro area countries covering about 60% of the euro area consumption basket over the period 2010-2019, we document new findings on consumer price rigidity in the euro area: (i) each month on average 12.3% of prices change, which compares with 19.3% in the United States; when we exclude price changes due to sales, however, the proportion of prices adjusted each month is 8.5% in the euro area versus 10% in the United States; (ii) differences in price rigidity are rather limited across euro area countries but much larger across sectors; (iii) the median price increase (resp. decrease) is 9.6% (13%) when including sales and 6.7% (8.7%) when excluding sales; cross-country heterogeneity is more pronounced for the size than for the frequency of price changes; (iv) the distribution of price changes is highly dispersed: 14% of price changes in absolute values are lower than 2% whereas 10% are above 20%; (v) the overall frequency of price changes does not change much with inflation and does not react much to aggregate shocks; (vi) changes in inflation are mostly driven by movements in the overall size; when decomposing the overall size, changes in the share of price increases among all changes matter more than movements in the size of price increases or the size of price decreases. These findings are consistent with the predictions of a menu cost model in a low inflation environment where idiosyncratic shocks are a more relevant driver of price adjustment than aggregate shocks.

    Keywords: price rigidity, inflation, consumer prices, micro data.

    JEL codes: D40, E31.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 27
2022-04-04

Producer and consumer price rigidity: the case of Lithuania

  • Abstract

    I provide the first statistics on producer and consumer price rigidity in Lithuania based on HICP and PPI item-level databases covering about 73% and 99.5% of their respective weights between 2010 and 2018. Producer prices are much more flexible than consumer prices, with an average monthly frequency of price change of 58% versus 18%. Contrariwise, the average size of price increases and decreases is higher in the HICP, reaching about 17-18% in absolute terms, whereas it is 7.5% in the PPI. In both price families, changes in item-level inflation are primarily due to variations in the size of price changes. However, the sources of these size changes are substantially shaped by shifts in the share of the number of price increases in the total.

    JEL Codes: D40, E31, E50.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 100
2022-01-25

State-Contingent Forward Guidance

  • Abstract

    This paper proposes a new strategy for modeling and solving state-dependent forward guidance policies (SCFG). We study its transmission channels using a DSGE model with search and matching frictions in which agents account for the fact that the SCFG is an endogenous regime-switching system. A fully credible SCFG causes a boom in inflation and output but no rapid exit from the ZLB. Thus, the transmission of its effects is primarily through the realization of additional ZLB periods more than through changes in expectations. We next consider the implications of imperfect credibility. In this case of uncertainty, an SCFG is less impactful. Finally, using counterfactual experiments on the December 2012 FOMC statement, we find that it led to about 1.5 pp gain in unemployment and 0.5 pp in inflation.

    Keywords: New Keynesian model, Search and matching, ZLB, Forward guidance.

    JEL codes: E30, J60.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 93
2021-10-12

The Effect of the Euro Changeover on Prices: Evidence from Lithuania

  • Abstract

    At the aggregate level, I find that the euro changeover did not lead to a significant change in the overall inflation rate between 2015 and 2019 in Lithuania. When the measures are diversified, however, some inflationary effects emerge in sub-categories. I therefore analyze this heterogeneity at the disaggregated level using a large sample of prices that constitutes the CPI from 2010 to 2018. I show that significant price changes have been confined to the low-weighted components of the HICP. This explains why a spike in the overall price level did not occur at the time of the changeover.

    Keywords: Euro changeover, synthetic difference-in-differences, regression discontinuity in time, price changes.

    JEL codes: E31, F33, L11.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 79
2020-10-08

Euro Area Monetary Communications: Excess Sensitivity and Perception Shocks

  • Abstract

    We explore new dimensions of the ECB’s monetary communications using the Euro Area Monetary Policy Event-Study Database (EA-MPD) built by Altavilla et al. (2019). We find that three new factors are needed to capture an excess sensitivity of long-term sovereign yields around monetary announcements. "Duration" surprises cause variations in real long-term rates and are mainly transmitted by term premiums. The "Sovereign spread" and "Save the Euro" surprises greatly influence the long-term yields of the periphery countries. These effects are difficult to reconcile with classic monetary policy shocks. We therefore study their underlying nature and discover that they have the characteristics of "Information", or what we label "Perception" shocks.

    Keywords: Monetary surprises, Event-study, Excess sensitivity, Perception shocks, High-frequency Identification.

    JEL Codes: E43, E44, E52, E58, G12.