Bank of Lithuania
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No 50

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 31

Households' inflation expectations in Lithuania: A First look and overview

  • Abstract

    We document a number of novel stylised facts about Lithuanian households' inflation expectations. Inflation expectations of Lithuanian households are significantly above the recent observation of actual inflation. On average, year-on-year inflation was around 4 percent from 2004 to 2023. However, one-year-ahead inflation expectations of households over the same period were on average 16.9 percent. Although we observe a clear upward bias in inflation expectations, there is significant co-movement between actual inflation and inflation expectations of households. Additionally, we find that over the economic boom, inflation expectations are higher than inflation perceptions, a finding that reverses over the economic downturn. We build a VAR model to analyse whether and how inflation, households' inflation expectations/perceptions and unemployment are linked. We show that structural shocks to inflation expectations play a minor role in overall inflation and unemployment dynamics.

    Keywords: Households' inflation perceptions, inflation expectations.
    JEL Classification: C83, D12, E21, E31.

No 24

Natural real rates of interest across euro area countries: Are R-stars getting closer together?

  • Abstract

    Using two different methodologies, we estimate time-varying natural real rates of interest for a majority of euro area (EA) countries, including Lithuania. We find that natural real rates have been declining, particularly since 2008, albeit to different extent across EA countries. Lower rates could (at least partly) be explained by lower productivity and population growth. In line with previous literature, we find evidence of a substantial dispersion of the natural interest rate across EA economies. This became especially evident during the financial crisis of 2008-2009 and the sovereign debt crisis of 2010-2012, while estimates of natural rates tend to converge during "calm" periods. Estimates of natural rates for Lithuania were significantly above the estimates of core EA countries over 2002-2008, but this has changed after the crisis. From 2011 the estimates of natural rates for Lithuania tend to be close to the average for EA countries.

    JEL Codes: C32, E32, E43, E52.

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

No 80

Assessing the impact of macroprudential measures: The case of the LTV limit in Lithuania

  • Abstract

    In this paper, we adopt a dual micro-and-macro simulation strategy to assess the impact of introducing (or changing) the LTV limit. Due to the nature of borrower-based macroprudential measures, to assess this impact we need to use borrower-level micro data. Tightening (or loosening) the LTV limit increases the share of borrowers constrained by the policy measure in question; thus, the overall impact depends on initial market conditions. We find that the introduction of an LTV limit of 85 % in 2011 had a modest short-term impact on economic activity because the new regulatory limit was non-binding for most borrowers at the time. We estimate that if the LTV limit would not have been introduced, the household loan portfolio would have grown on average 1.5 percentage points faster per year (over 2012-2014). This would have led to a 0.5 percentage point higher housing price growth and a 0.2 percentage point higher real GDP growth. When the macroprudential LTV limit is binding for a significant portion of borrowers, lowering the LTV limit at current market conditions has a much more pronounced effect. We show that if the LTV limit had been implemented at the end of 2004, it would have substantially helped in tempering the credit and housing boom, albeit at the cost of lowering economic growth.

    Keywords: Financial stability, Macroprudential policy, Borrower-based macroprudential policy instruments, LTV limit.

    JEL Codes: C32, C53, E58, G28.

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

No 29

CBDC – in a whirlpool of discussion

  • Abstract

    The topic of central bank digital currency (henceforth - CBDC) has recently gained significant share of attention among policy makers and academics. A wide range of CBDC setups are discussed from the universally accessible central bank accounts or digital tokens to less extreme suggestions of only partly broadening central bank balance sheet access by providing CBDC to wholesale consumers or getting private sector to mediate in the process by providing synthetic CBDC.

    This paper recalls the possible CBDC implementation types that are discussed in the current context; reviews some of the discussions among those researching the topic; gives a brief overview of the next-step initiatives taking place among central banks with a potential to lay ground for the practical CBDC implementation; and discusses the main policy implications from financial stability and monetary policy perspectives.

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

No 28

Lithuanian house price index: modelling and forecasting

  • Abstract

    Timely monitoring of the housing market developments in Lithuania is one of the key elements in the analysis framework of the macroprudential authority aiming to contribute to financial stability in Lithuania. In this paper, we addressed three important questions related to Lithuanian house prices, namely, whether house prices are under- or over valuated, which explanatory variables have the biggest impact on the growth of house prices and what the future development of the Lithuanian house price index could be. Three separate modelling and forecasting exercises were performed in order to tackle these questions. The first exercise employs the vector error correction modelling (VECM) approach to assess under- or overvaluation of the house prices. We then use an autoregressive distributed lag model (ARDL) to evaluate which explanatory variables have the biggest impact on house price growth. As the last exercise, we develop a suite of models that are used to forecast future development of the house price index. The analysis presented in this paper may be viewed as a further step towards more formalised modelling and forecasting of the Lithuanian house price index.

    JEL Codes: C22, C32, C53, E37, R30.

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