Publications

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.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this paper, we investigate different methods of assessing business cycles for the European Union in general and the euro area in particular. First, we conduct a Monte Carlo experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analyzed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, we used the structural model’s parameters calibrated to the euro area’s real GDP and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models consisting of popular Hodrick-Prescott, Christiano-Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. We find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and business cycles; and (iii) the best-performing Monte Carlo approaches provide a reasonable combination as the suite of models. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the business cycle for real GDP. Second, we estimate the business cycles for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. Our analysis also confirms that the business cycles of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronised with the aggregate euro area.

JEL Codes: C31, E27, E32.

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

This paper examines changing ECB communication and how it has impacted euro area financial markets over the past two decades. We applied a combination of topic modelling and sentiment analysis for over 2000 public ECB Executive Board member speeches, as well as over 200 ECB press conferences. Topic analysis revealed that the ECB’s main focus has shifted from strategy and objectives, at the inception of the euro area, to various policy actions during the global financial crisis and, more recently, to instruments and economic developments. Sentiment analysis showed an expected trend of a more negative communication tone during periods of turmoil and a gradual shift to a more dovish monetary policy tone over time. Regression analysis revealed that sentiment indices had the expected impact on financial market indicators, while press conferences showed substantially stronger effects than speeches.

JEL Codes: C80, E43, E44, E58, G12.

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

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.

This document explores the incidence of the minimum wage in Lithuania. The descriptive analysis exploits high-frequency data on monthly labor income coming from Social Security records between July 2013 and July 2020 to characterize (i) the evolution of the monthly minimum wage, (ii) the percentage of workers who earn the minimum wage, (iii) the bite of the minimum wage in the wage distribution, and (iv) the heterogeneity of the findings with respect to gender and age. The evidence shows that the minimum wage was raised 7 times with an average (real) increase of 7.3% and, on average, less than 10% of the workers earn at most the minimum wage but low-pay incidence is around 20%. In terms of the impact of the wage distribution, the minimum wage relative to the average wage in the economy fluctuates between 45 and 50 percent. Females and young workers exhibit a larger low-pay incidence and minimum wage bite.

JEL Codes: J38, J48

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

The article analyzes recent developments in investments in Lithuania using a broad set of possible drivers, including EU funds. We apply a Bayesian VAR setup with data from 1997Q1 to 2019Q4. We also examine and compare business vs. government investments and different types of investments, especially innovative investments. We find that total investments are basically driven by the data on business investments. The main outcomes are mostly in line with the literature, but we do see some crucial differences across types. Key results include: (1) a small role for lending rates as compared to other factors, largely limited to the global financial crisis; (2) the crucial role of demand-side variables, i.e. foreign demand or private consumption; (3) pro-cyclicality in government investments and a positive correlation with business investments; (4) the importance of uncertainty for some sectors, that positively drives only the more innovative/intangible investments; and (5) despite the fact that EU funds do feed investments, there is a crowding-out in the short run for business-related investments, while there is some positive contribution to public investments.

JEL Codes: E32, D24, D61, C32.

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

This article examines possible factors related to the rate of suicide in Lithuania, which is the highest in Europe and one of the highest worldwide. Using statistical methods, we select possible determinants from the literature in the fields of economics, psychology and sociology. We look at annual data from 1994 to 2016 for the Baltic States, with a specific focus on Lithuania. The main factors linked to suicide in the region seem to be GDP growth, demographics, alcohol consumption, psychological factors and global warming. For Lithuania in particular, other macroeconomic variables (especially linked to the labor market) may matter. The percentage of rural population does not seem to be a key robust factor.

JEL Codes: I15, I31, J11, J17, O15.

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

The aim of this paper is to assess how relevant is the topic of sovereign bond valuations in official ECB Executive Board member speeches and, in particular, under what circumstances do ECB officials begin communicating the driving factors of sovereign bond pricing. For this purpose, we downloaded over 2000 public ECB Executive Board member speeches and applied various text mining techniques. The visual analysis revealed that the importance of the topic of sovereign bond pricing and related risk factors in ECB officials’ speeches has greatly fluctuated over time. The main structural break points were linked to the financial market turbulences, but this topic, possibly due to the introduction of sovereign bond purchases, remained relatively popular even after stress episodes. The linkages between the publicly communicated terms of sovereign bond pricing and related risk factors were rather complex and change in respect to the market situation. Meanwhile, the sentiment balance of the credit risk factor was usually on the negative side, while the ones of other terms were much more neutral.

JEL Codes: C80, E43, E58, G12.

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

This paper reports new data on the household balance sheet and the consumption situation in Lithuania. It uses a unique Household Finance and Consumption Survey (HFCS) dataset, which collects detailed information about different asset classes and outlines the composition of the household balance sheet in Lithuania. At 93.2%, the homeownership rate in Lithuania is the highest in Europe. Real assets correspond to the highest share of households’ wealth and generate a median net wealth of 46 000 €. Lithuanian households participate poorly in financial assets, with only deposits and individual insurance/pensions generating more significant aggregate values. Household participation in debt markets is also limited in Lithuania, with only 11.7% of households having some mortgage-based liabilities. Lithuanian households spend a significant share of their income on food and utilities. This share is among the highest in Europe. A large number of Lithuanian households can be characterized as "hand-to-mouth" households, as they own a significant amount of wealth in illiquid real estate and very little wealth in liquid financial assets.

JEL Codes: D1, D3.

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

This paper analyzes the transition to a low-carbon economy and its effects on macroeconomic quantities and asset prices. Empirically, we document that the relative valuation of fossil fuel firms has significantly declined with the rise of climate change risk awareness. We develop a macro asset pricing model for the climate transition that matches this empirical fact and allows us to characterize the dynamics of macroeconomic aggregates and asset prices during and after the transition. In particular, we analyze (i) firm valuation dynamics, (ii) climate policy risk premia, (iii) capital reallocation between sectors, and (iv) the behavior of oil prices.

JEL Codes: E2, E3, G12, Q43.

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

We study the behaviour of Lithuania relative to other 25 EU countries, looking specifically at convergence in terms of GDP per capita and its growth accounting components: capital accumulation, labour and its subcomponents, i.e. participation and employment, and the Total Factor Productivity (TFP). We find that Lithuanian Real GDP per capita shows indeed a convergence path similar to the other Baltic States and they all belong to the second club (includes part of the periphery and the other new member states). The convergence paths of labour or capital accumulation do not seem significantly different compared to the ones of other EU members. The Lithuanian transition path in TFP has become plateau after the crisis but this is seemingly not a divergence factor. Two components show noticeable changes in behaviour after 2010: the growth in total factor productivity (TFP) considerably slows down, and the employment-population ratio appears to increase accounting for around one third of the annual GDP growth in Lithuania. In addition, we explore several transition scenarios for Lithuania to the EU-25 average.

JEL Codes: O47, F15, F45.

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

In this paper, we first provide a comprehensive account of the relationship between cross-country differences in aggregate employment and disaggregated differences in worker flows along the life cycle. To this end, we use survey micro-data for 31 European countries, and estimate the life-cycle profiles of transition probabilities across employment, unemployment and non-participation for each country. We develop a decomposition measuring the contribution of these transition probabilities to aggregate employment differences. We find substantial cross-country and cross-gender heterogeneity with respect to the role of worker flows between each labor market state.

JEL Codes: E02, E24, J21, J64, J82.

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

Despite Lithuania’s household income inequality being among the highest in the European Union (EU), little empirical work has been carried out to explain such disparities. In this article, we use the EU Statistics on Income and Living Conditions sample micro data. We confirm that income inequality in Lithuania is high compared to the EU average and find that it is robust to inequality measure or equivalence scale used. We have also decomposed household disposable income inequality by subgroups and factors. We find that the number of employed household members in Lithuania’s households affects income inequality more as compared to the EU. It is related to a larger labour income, and self-employment income in particular, contribution to inequality in Lithuania as opposed to the EU. Moreover, transfers and taxes have a smaller impact on reducing inequality in Lithuania than in the EU.

JEL Codes: D31.

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

In this paper, we investigate the Exchange Rate Pass-Through (ERPT) to import and consumer prices in the three Baltic states. We apply reduced form equations first. Then, to look at measures of shock-dependent ERPT, we use Bayesian VARs with zero and sign restrictions and a local projection exercise, using common euro area shocks. We find that results from reduced form equations are in line with the ERPT literature. As for shock-dependent ERPTs, the magnitudes are overall bigger than in the literature in the case of import prices. They get smaller for consumer prices and even smaller if we remove energy and food prices.

JEL Codes: E31, F3, F41.

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

In this paper, we develop and describe quarterly data on disaggregated sectors in Lithuania which covers the period 1998-2018. The data is useful for empirical studies requiring panels with a large number of time observations and a large number of cross-sectional units. We follow the NACE2 level of disaggregation in developing our data, thus allowing us to combine the data with world input-output tables which we in turn use to identify the hubs and the main importing and exporting sectors within the economy. The data is then used for forecasting the growth rate of GDP. There is a substantial increase in the degree of covariation among sectoral production growth rates, which is observed using a split sample around 2008. When we apply factor methods, we find that this strong covariation can be explained by a few factors which are closely correlated to the growth of the retail and wholesale sectors. For GDP growth, the forecasts we consider are the diffusion index forecasts produced using a few indexes that summarize sectoral data, and the forecasts produced using the production growth of selected hubs and importing and exporting sectors. We find that the diffusion indexes and the production growth of sectors that make heavy use of imported inputs in their production have interesting forecasting power for the growth rate of GDP in the 2006-2011 and 2012-2018 periods.

JEL Codes: E27, E37, C3, C67.

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

This paper applies Latent Dirichlet Allocation (LDA), a machine learning algorithm, to analyze the transcripts of the U.S. Federal Open Market Committee (FOMC) covering the period 2003 – 2012, including 45,346 passages. The goal is to detect the evolution of the different topics discussed by the members of the FOMC. The results of this exercise show that discussions on economic modelling were dominant during the Global Financial Crisis (GFC), with an increase in discussion of the banking system in the years following the GFC. Discussions on communication gained relevance toward the end of the sample as the Federal Reserve adopted a more transparent approach. The paper suggests that LDA analysis could be further exploited by researchers at central banks and institutions to identify topic priorities in relevant documents such as FOMC transcripts.

JEL Codes: E52, E58, D78.

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

In this paper we analyse the price competitiveness of the Italian regions by computing the Real Effective Exchange Rate (REER) for each region, deflated by CPI and vis-à-vis the main partner countries. We use them to look for the medium-term determinants, finding significant heterogeneities in the role of government consumption and investment expenditure. Government consumption has an extremely negative effect on competitiveness in North-Eastern Italy, Southern Italy and Lazio. Investment plays a negative role especially in the North-West, while it can be positive for competitiveness in Lazio and Southern Italy. We also find that the transfer theory does not necessarily hold and it even behaves in the opposite direction in case of North-Eastern Italy and Lazio. Lastly, we show that an increase in the regional price competitiveness influences regional growth positively only in the long run and spillovers may play a role.

JEL Codes: E62, F31, F41, R11.

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

We shed new light on the macroeconomic effects of rising temperatures. In the data, a shock to global temperature dampens research and development (R&D) expenditure growth. This novel empirical evidence is rationalised within a stochastic endogenous growth model. In the model, Temperature shocks undermine economic growth via a drop in R&D. Moreover, temperature risk generates welfare costs of 13.50% of lifetime utility. The government can offset these welfare costs by subsidizing investment with 1.02% or R&D expenditure with 0.52% of total public spending, respectively. Alternatively, it can levy a lump-sum tax on households which finances 0.64% of total public spending.

JEL Codes: E30, G12, Q00.

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

Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings. In particular, an additional network penalty can be added on top of another penalty term, such as a Lasso penalty. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We show detailed simulation results of such an algorithm. The algorithm performs well in a variety of settings. We also briefly describe the R-package that we developed for this purpose, which is publicly available.

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

In this paper we offer a unique firm-level view of the empirical regularities underlying the evolution of the Lithuanian economy over the period of 2000 to 2014. Employing a novel data-set, we investigate key distributional moments of both the financial and real characteristics of Lithuanian firms. We focus in particular on the issues related to productivity, firm birth and death and the associated employment creation and destruction across industries, firm sizes and trade status (exporting vs. non-exporting). We refrain from any structural modeling attempt in order to map out the key economic processes across industries and selected firm characteristics. We uncover similar empirical regularities as already highlighted in the literature: trade participation has substantial benefits on firm productivity, the 2008 recession has had a cleansing effect on the non-tradable sector, firm birth and death are highly pro-cyclical. The richness of the dataset allows us to produce additional insights such as the change in the composition of assets and liabilities over the business cycles (tilting both liabilities and assets towards the short-term) or the increasing share of exporting firms but the constant share of importing ones since 2000.

JEL Codes: D22, D24, E30, J21, J24, J30, L11, L25.

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

Do inter-sectoral linkages of intermediate products affect the spread of sectoral shocks at the aggregate level in Lithuania, a small and open economy? We answer this question by: i) constructing the domestic sector-by-sector direct requirements table using the Lithuanian interindustry transactions tables, and ii) applying Acemoglu et al. (2012)'s network-based methodology and Gabaix and Ibragimov (2011)'s modified log rank-log size regression to analyse the nature of inter-sectoral linkages. Our results indicate that the direct and indirect inter-sectoral linkages cause aggregate volatility to decay at a rate lower than √n - the rate predicted by the standard diversification argument. Furthermore, indirect linkages play an important role in the above-mentioned process, supporting the findings of Acemoglu et al. (2012). These results suggest that the inter-sectoral network of linkages represent a potential propagation mechanism for idiosyncratic shocks throughout the Lithuanian economy.

JEL Codes: C13, C46, C67, E00.

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

In this paper we apply the analytical integrated accounts framework to conduct a conceptual analysis of essential macrofinancial linkages. In particular, we analyse the macroeconomic mechanism of the creation of purchasing power through bank credit, explore the partial self-financing property of bank credit and the links between bank credit and money creation, and discuss the role of debt accumulation as a powerful demand-side driver of growth. We argue that creation of money and purchasing power is an indispensable corollary of bank credit issuance. Contrary to conventional wisdom, credit is not predicated on existing savings. It directly adds to domestic demand, which translates into some combination of stronger domestic economic activity, stronger foreign economic activity or higher prices, with particular configuration depending on the structural features of the economy. However, credit-driven growth may result in a systemic over-reliance on continuous debt accumulation and poses the risk of deep structural imbalances and balance sheet recessions.

JEL Codes: E51, E58, G21.

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

This paper investigates the importance of unemployment and credit in determining the potential level of real activity for a small-open economy with a low degree of financialization. We estimate a multivariate unobserved component model (MUC) to derive the potential output and its associated output gap for the Lithuanian economy. The model is estimated via Bayesian methods and the time-paths of unobserved variables are extracted via the Kalman filter. We find that the inclusion of unemployment into the MUC model substantially improves the estimates of output gap in real-time. Once information about unemployment is accounted for, adding information about credit does not substantially alter either the estimates of output gap or its performance in real time. We uncover a strong negative correlation between the model-implied unemployment gap (without credit) and real credit growth. This explains the relatively muted impact of the financial variable on the level and dynamics of the output gap. Data revisions appear not to be the primary source of revisions on output gaps estimates.

JEL Codes: C11, C32, E24, E32.

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

The view that an institutional structure causes rigidities in the labour market is broadly accepted by policy makers. This assessment is conventionally based on unemployment theories that establish a link between labour market institutions and unemployment in the long run. Empirical research engages in investigation of whether the theoretical link between unemployment and labour market institutions could be proved to prevail. This paper provides an econometric analysis of the determinants of unemployment in the long run in a set of Central and Eastern European countries for the period of 2002–2012. Evidence that an institutional structure causes rigidities in the labour market and has a direct effect on the unemployment rate in these economies is found in this study. A set of non-structural indicators, accounted by macroeconomic shocks, also prove to have effects on the labour market outcomes. From a policy making perspective, such implications suggest that structural labour market reforms and increases in the overall flexibility of the labour market in these economies are necessary to bring unemployment rates down.

JEL Codes: E02, J60.

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

We extend OGRE, the overlapping generation model developed by Baksa and Munkacsi (2016) by adding openness. We then employ the model to explore how the macroeconomic effects of aging, assumed to manifest itself as a decrease in the mortality rate, can be counteracted through public policies. The extended version inherits the previous modelling features of OGRE allowing us to also account for the impact openness has on the effectiveness of the considered policies.

JEL Codes: E24, E26, F41, H55, J11, J46.

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

This paper deals with the macroeconomic responses to labour market reforms; something of utmost importance in times of monetary policy reaching its limits to affect real economy. We shed more light on the plausible macroeconomic reactions to the ex ante (planned but not implemented yet) reforms in the labour market, taking a currently proposed Social Model in Lithuania as an example. Not only contributing to the current debate on the efficacy of announced structural reforms, we also add to the literature on policy evaluation by assessing reforms from a global perspective. Omission of an international dimension could lead to seriously biased results on policy effects for any open and small economy. Taking trade connectivity and openness into account, we demonstrate macroeconomic reactions to shocks in unemployment benefits, active labour market policies, and tax wedge on the reforming economy. We contrast the results with the approach when global interdependencies are ignored – still a standard practice. Using a satellite model for the intermediate trade, we link the global framework with the sectoral extensive margin, which changes some of the initial findings. A discussion on counterfactuals, which use both cross-sectional and temporal dimensions to tackle anticipation effects, is also presented.

JEL Codes: C33, C54, E62, J38.

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