Publications

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.

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.

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.

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.