lithuanian economic review 2015 · 2015 december 2015 lithuanian economic review . author: rasa...

2015

DECEMBER

2015

LITHUANIAN ECONOMICREVIEW

ISSN 2029-8471 (online)

Lithuanian Economic Review analyses the developments of the real sector, prices, public finance and credit in Lithuania, as well as the projected development of the domestic economy. The material presented in the Review is the result of statistical data analysis, modelling and expert assessment. The Review is prepared by the Bank of Lithuania.

© Lietuvos bankas, 2015

Reprinting is allowed only for education and non-commercial purposes, if the source is indicated.

During the preparation of the Lithuanian Economic Review, the data of the Bank of Lithuania, Statistics Lithuania, the European Central Bank, Eurostat, the International Monetary Fund, Bloomberg and other data published up to 13 November 2015 were used.

Contents

ECONOMIC OUTLOOK ........................................................................................................................................................................... 3

I. INTERNATIONAL ENVIRONMENT....................................................................................................................................................... 5

II. MONETARY POLICY OF THE EUROSYSTEM ................................................................................................................................... 8

III. REAL SECTOR ................................................................................................................................................................................. 11

IV. LABOUR MARKET ........................................................................................................................................................................... 13

V. EXTERNAL SECTOR ........................................................................................................................................................................ 15

VI. PRICES AND COSTS ....................................................................................................................................................................... 17

VII. FINANCING OF THE ECONOMY .................................................................................................................................................... 19

VIII. GENERAL GOVERNMENT FINANCE ............................................................................................................................................ 21

ANNEXES .............................................................................................................................................................................................. 24

ANNEX 1. Factors that determine investment ................................................................................................................................... 24

ANNEX 2. The effect of minimum wage increase on macroeconomic variables: survey results of Lithuanian enterprises ................. 29

ANNEX 3. Working age population and labour force development and outlook ................................................................................. 33

ANNEX 4. Analysis of profit indicators calculated from national accounts ......................................................................................... 38

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List of tables and charts

Table

GDP and inflation developments in selected advanced and emerging market economies .......................................................................................................... 5

Charts

Chart 1. Developments of purchasing managers’ indexes ............................................................................................................................................................ 5 Chart 2. Annual inflation in the United States and the euro area................................................................................................................................................... 5 Chart 3. Russia’s real GDP development ...................................................................................................................................................................................... 6 Chart 4. Development of real GDP of Lithuania’s export partners................................................................................................................................................. 6 Chart 5. Commodity price indexes ................................................................................................................................................................................................. 6 Chart 6. ECB interest rates and 6-month EURIBOR ..................................................................................................................................................................... 8 Chart 7. ECB’s benchmark interest rate and inflation .................................................................................................................................................................... 8 Chart 8. Annual yields on euro area’s government bonds with a residual maturity close to 10 years and issued in national currencies .................................... 8 Chart 9. Average interest rate on MFIs’ new housing loans .......................................................................................................................................................... 9 Chart 10. Average interest rate on MFIs’ new loans to non-financial undertakings ...................................................................................................................... 9 Chart 11. Contributions to the development of real GDP by expenditure approach ................................................................................................................... 11 Chart 12. Exports of services broken down by markets............................................................................................................................................................... 11 Chart 13. Manufacturing sales ..................................................................................................................................................................................................... 11 Chart 14. Contributions to the development of nominal GDP by income approach .................................................................................................................... 12 Chart 15. Construction output and construction value added (at constant prices) ...................................................................................................................... 12 Chart 16. Developments in jobs ................................................................................................................................................................................................... 13 Chart 17. Unemployment rate (seasonally adjusted) ................................................................................................................................................................... 13 Chart 18. Labour supply indicators (seasonally adjusted) ........................................................................................................................................................... 13 Chart 19. Lithuania’s exports broken down by country groups (three-month moving sums) ...................................................................................................... 15 Chart 20. Evolution of the refining margin of AB ORLEN Lietuva................................................................................................................................................ 15 Chart 21. Components of the current account balance ............................................................................................................................................................... 15 Chart 22. Contributions to annual HICP inflation ......................................................................................................................................................................... 17 Chart 23. Developments of Brent crude oil prices and fuel prices in Lithuania ........................................................................................................................... 17 Chart 24. Price developments in individual categories of the consumption basket (excluding fuel) ........................................................................................... 17 Chart 25. Contribution to changes in MFIs’ loan portfolio ............................................................................................................................................................ 19 Chart 26. Interest rates ................................................................................................................................................................................................................. 19 Chart 27. Loans to businesses, broken down by economic activity ............................................................................................................................................ 19 Chart 28. Contributions to the growth of general government balance broken down by subsectors .......................................................................................... 21 Chart 29. Contributions to the growth of general government revenue ....................................................................................................................................... 21 Chart 30. Annual changes of personal income tax, social security contributions and theoretical tax base ................................................................................ 21 Chart 31. Contributions to the growth of general government spending ..................................................................................................................................... 22 Chart 32. Change in investment as a share of general government expenditure in Poland and the Baltic countries in 2010–2013 (COFOG data) ................ 22 Chart 33. Contributions to the development of general government debt ................................................................................................................................... 22

Abbreviations

AB public company

APP Asset Purchase Programme

CIS Commonwealth of Independent States

COFOG Classification of the Functions of Government

EC European Commission

ECB European Central Bank

ESCB European System of Central Banks

EU European Union

EURIBOR Euro Interbank Offered Rate

EUROPOP European Population Projections

Eurostat statistical office of the European Union

GDP Gross Domestic Product

HICP Harmonised Index of Consumer Prices

IMF International Monetary Fund

LTRO Longer-Term Refinancing Operation

MFI Monetary Financial Institutions

MRO Main Refinancing Operation

NACE Classification of Economic Activities

OPEC Organization of Petroleum Exporting Countries

TLTRO Targeted Long-Term Refinancing Operation

US United States of America

VAT Value-Added Tax

VĮ state enterprise

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ECONOMIC OUTLOOK

Compared to the past few years, the pace of Lithuania’s economic growth has roughly halved this year, primarily due to the

downside effects of the international environment. Stubbornly low prices for energy commodities and continuing economic

sanctions have dragged down economic activity in Russia and in the countries with economic and financial ties to Russia. Real GDP

is presumed to have contracted by approximately 4 per cent in Russia this year and the health of other CIS economies has also

taken a noticeable turn for the worse. A substantial fall in these countries’ imports and depreciated currencies act as a drag on the

performance of Lithuania’s transport, storage and wholesale undertakings involved in re-export operations in particular as transport of

freight to the Eastern countries accounts for a significant part of services provided by these businesses.

Even so, export dynamics are not too grim. Real merchandise exports only fell slightly as compared to last year when Russia’s

economic downturn started to take hold. The decline in exports to the Eastern countries is offset to a large extent by the growth of

exports to other countries, in particular the EU, which is driven inter alia by improvements in economic well-being in the European

countries and by exporters’ efforts to discover new markets. It is noteworthy that the real exports of services continued to increase. A

decline in eastward exports of transport services, which represent a large share of the total exports of services, was accompanied by

an increase in such services provided to other countries, which mitigated the effects from the changed international environment.

The international context is not the only factor behind the softer performance of the economy. In particular, the pace of

growth in the construction sector has slowed down this year. The annual rate of growth in the value added created in this sector

almost halved in the first half of this year, from approximately 14 per cent recorded last year. The slowdown is due to infrastructure

and non-residential construction, which has stopped growing. Home construction is the only segment to show sizeable growth.

Investment in residential buildings, unlike investment in other construction facilities, has already recovered to the pre-downturn level.

However, this investment also remains surrounded by some uncertainty. The growth of activity in the housing market is likely to

moderate from the pace observed in recent months and the supply of newly built homes is relatively abundant. Less intensive

development of residential investment would further dampen activity in the construction sector.

As in the past few years, private consumption continues to underpin economic growth. The labour market has remained

immune to the slowdown in the country’s economic development. Employment in transport and trading enterprises, i.e. in the eco-

nomic entities that have been hardest hit by the economic downturn in Russia, has lost its previous growth pace but is not falling,

either. Elsewhere in the economy, jobs are growing significantly in number. Hence the demand for labour has not decreased, which

has a positive effect on wage dynamics. With the number of vacancies climbing and unemployment falling, wage growth, which is

also supported by the increase of the minimum monthly wage, continues broadly unchanged from last year, which paves the way for

households to step up consumption. The growth of consumption is rather strong, despite continuing uncertainty over the country’s

economic outlook, which has been getting gloomier for quite a while.

Domestic demand is expected to keep growing at a robust pace in the forecast period. However, the pace of economic

growth will still remain affected by the external environment. Foreign demand, i.e. the imports of trade partners, is forecast to

rebound in 2016 after this year’s fall hence the growth of Lithuania’s exports should pick up somewhat next year. Expected improve-

ments in the international environment should also lead to faster growth of the entire economy. Lithuania’s real GDP is projected to

grow by 1.7 per cent in 2015 and by another 2.9 per cent next year. The forecast of economic growth in 2016 has been revised down

(from 3.2%) due to a more cautious approach to the outlook for the international environment and, in particular, Russia’s outlook.

Downside risks to Lithuania’s economic growth continue to mount amid lingering uncertainty over Russia’s economic development

and concerns about the growth outlook in other emerging economies. Weaker than expected global economic growth would have

negative implications, both direct and indirect, for the growth of Lithuania’s economy as it would lead to a worsening of the outlook for

economic growth in foreign trade partners.

Trends in prices remain favourable for consumers. Annual inflation has been negative, i.e. prices have been lower than their

year-earlier levels, since the end of 2014. The year 2015 is coming to an end and it is therefore obvious that the average annual

inflation rate this year will also have a negative sign and is projected to be –0.6 per cent. The fall in prices this year has mainly been

driven by prices for energy, in particular fuel, which have tumbled, pulled down by the slump in crude oil prices late last year and

early this year. Annual inflation should return to positive territory in the near future as the base effect from the previous fall in crude oil

prices fades out. However, inflation next year should be low given the continued environment of low inflation and will therefore have

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virtually no eroding effect on household purchasing power. In 2016, the average annual inflation rate is projected to be 1.4 per cent. In

particular, the upward trajectory of non-energy prices will not be steep while prices for energy may continue to fall, albeit at a much

slower pace than this year.

Outlook of Lithuania’s economy in 2015–2016

December 2015 projectiona September 2015 projection

2014 2015b 2016b 2014 2015b 2016b

Price and cost developments (annual percentage changes)

Average annual inflation, as measured by the HICP 0.2 –0.6 1.4 0.2 –0.5 1.5

GDP deflatorc 1.2 –0.3 1.2 0.9 –0.3 1.3

Wages 4.7 4.8 5.0 4.7 4.5 4.9

Import deflatorc –3.0 –4.8 0.2 –3.1 –4.9 1.4

Export deflatorc –2.5 –2.9 0.1 –2.5 –3.4 1.3

Economic activity (constant prices; annual percentage changes)

Gross domestic productc 3.0 1.7 2.9 3.0 1.6 3.2

Private consumption expenditurec 4.1 4.6 4.1 5.6 4.0 4.0

General government consumption expenditurec 1.2 1.8 0.9 1.6 1.8 1.2

Gross fixed capital formationc 5.3 7.5 2.6 7.8 5.9 3.3

Exports of goods and servicesc 2.9 0.8 3.3 3.4 0.7 4.0

Imports of goods and servicesc 2.8 7.3 3.6 5.6 3.4 4.2

Labour market

Unemployment rate (annual average as a percentage of labour force)

10.7 9.4 9.0 10.7 9.6 9.1

Employmentd (annual percentage change) 2.0 1.2 0.2 2.0 1.3 0.3

External sector (as a percentage of GDP)

Balance of goods and services 1.9 –1.5 –1.9 0.0 –0.9 –1.1

Current account balance 3.3 –2.5 –3.5 0.1 –1.9 –2.8

Current and capital account balance 6.0 0.2 –1.6 2.9 0.8 –0.9 a These projections of macroeconomic indicators are based on information made available by 13 November 2015. b Projection c Adjusted for seasonal and workday effects d National accounts data; employment in domestic concept

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I. INTERNATIONAL ENVIRONMENT

The developments observed in the international environment in

2015 were not favourable. As in the past several years, the growth of

emerging economies, especially commodity-exporting ones, continued

to lose steam and the acceleration of recovery in advanced economies

was not strong enough to offset that slowdown. The ongoing military

conflict in Ukraine, Greece’s precarious financial situation, Russia’s

economic downturn, China’s economic slowdown and high volatility in

commodity, currency and stock markets show that the economic

recovery of different global regions remains fragile and the global

economy is still vulnerable.

With the uncertainty surrounding the growth in the global eco-

nomic outlook, the world’s major central banks keep their bench-

mark interest rates at low levels and pursue accommodative

monetary policy. Some of them, in addition, implement non-

conventional quantitative easing measures. Despite market expec-

tations of monetary policy tightening in the United States, the Federal

Reserve left its benchmark interest rate unchanged until December

2015, whereas the central banks of the Eurosystem, Japan and Swe-

den continued to apply a mix of standard and non-standard instruments

of accommodative monetary policy. The renewal of the decline in

commodity prices in May, the worsening of expectations about the

future inflation path and the concomitant slowdown in China and a

number of other emerging economies created even more uncertainty

over the global economic recovery. In this context, the world’s major

central banks continued to maintain their accommodative monetary

policy stances and expectations of low-for-longer interest rates

strengthened further in the second half of the year.

Despite the slowdown recorded in the first quarter, the United

States continues to enjoy one of the fastest growth rates among

advanced economies. As usual, growth was led by domestic demand.

Household consumption was promoted by growth in real disposable

household income reflecting the health of the labour market and low

inflation, which remained below the Federal Reserve’s medium-term

target of 2 per cent. The period under review also saw improvements in

the property market, including the rebound in new home construction

and the increase in real estate prices. Business investment growth was

sluggish, primarily due to a slump in oil investment. Even though crude

oil production in the United States started to decrease, inventory levels

remained high and put downward pressure on global oil prices. Market

expectations of an imminent rise in the Federal Reserve’s benchmark

interest rate steered the US dollar upward, which contributed to the

decrease in the country’s exports and the widening of its trade deficit.

The euro area’s economy continues to recover, albeit at a

weaker pace than expected early in the year. Growth is weighed

down by the still high level of public sector indebtedness in the euro

area countries, sluggish implementation of structural reforms, muted

growth of investment and high level of unemployment (even though it

has declined moderately in the region, it still exceeds 20% in some of

the countries in the region). After a brief spell of faster gains between

May and July, annual inflation in the euro area returned to the negative

zone in September, mainly due to the renewed decline in energy

prices. Therefore, inflation remains below the ECB’s target rate of

inflation rates of below, but close to, 2 per cent over the medium term.

Even though the euro area’s exports followed an upward path (mostly

Germany’s), the forces of change in the external environment were far

from favourable, given the economic deterioration in the euro area’s

The IMF expects the growth of real GDP to gather pace in both emerging and advanced economies in 2016.

GDP developments and inflation in selected advanced and emerging market economies

2014 2015* 2016*

Real GDP change, per cent

Global 3.4 3.1 3.6

Advanced economies 1.8 2.0 2.2

USA 2.4 2.6 2.8

Euro area 0.9 1.5 1.6

Emerging market economies 4.6 4.0 4.5

China 7.3 6.8 6.3

Russia 0.6 –3.8 –0.6

Inflation, per cent

Advanced economies 1.4 0.3 1.2

USA 1.6 0.1 1.1

Euro area 0.4 0.2 1.0

Emerging market economies 5.1 5.6 5.1

China 2.0 1.5 1.8

Russia 7.8 15.8 8.6

Source: IMF.

* Forecasts.

The global composite purchasing managers’ index followed a downward path until September, mostly due to slowdown in growth in emerging economies, especially China.

Chart 1. Developments of purchasing managers’ indexes

Annual inflation remains below the target level both in the United States and the euro area.

Chart 2. Annual inflation in the United States and the euro

area

46

50

54

58

62

2012 2013 2014 2015

ChinaEuro areaUSAWorld

Source: "Markit“.

–1

0

1

2

3

4

2012 2013 2014 2015

Annual inflation in USA

Annual inflation in euro area (HICP)

Per cent

Sources: Eurostat, US Labor Force Statistics Bureau.

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–6

–4

–2

0

2

4

6

2012 2013 2014 2015

Source: Russian Federation Federal State Statistics Service.

Per cent, annual change

main export partners, in particular the emerging countries. Neverthe-

less, the IMF expects the euro area’s economy to avoid a decelera-

tion of growth next year thanks to the recovery of domestic demand.

These forecasts are underpinned by improvements in business and

consumer confidence indicators, consumer-friendly commodity price

trends, favourable developments in financing conditions and low

interest rates.

In 2015, Russia’s economy continued to deteriorate and the

slump in prices for commodities, particularly crude oil, damp-

ened the country’s chances of recovery. Russia’s real GDP

followed a downward path in the first half of the year, mainly due to

falling domestic demand. The decrease in real disposable income

and the contraction of purchasing power ate into household con-

sumption while limited access to financing and elevated uncertainty

over the country’s economic growth potential kept investment on a

downward path. Sliding commodity prices put pressure on the

rouble, contributing to its depreciation, which in turn led to a surge in

inflation and pushed up foreign debt service costs. The slump in oil

prices also dealt a heavy blow to public finances, which suffered

from a decline in federal budget revenue, a rise in deficit and a

significant depletion of the Reserve Fund. Mutual economic sanc-

tions, imposed by the EU and Russia until July 2015, were eventual-

ly extended. Russia decided to keep its sanctions in place until the

summer of 2016 while the EU extended its restrictions until January

2016. This means that Western capital markets will remain shut for a

number of Russia’s banks and undertakings in the near future,

hence external borrowing will remain limited and will continue to

dampen investment and the country’s economic growth potential. At

the same time, Western prohibitions related to the supply of tech-

nology and equipment should lead to challenges for the Russian

energy companies in developing oil exploration and other infrastruc-

ture projects.

Economic growth trends exhibited by Lithuania’s major

trade partners are uneven. The Baltic countries continue to feel the

pinch from the economic deterioration in Russia and the existing

trade restrictions, which weigh on the performance of tradable

sectors as the expansion of exports to new markets is not sufficient

to fully offset the decline in exports to Russia. Nevertheless, both

Latvia and Estonia recorded growth in real GDP driven by improve-

ments in domestic demand. Poland’s economy, supported by growth

in domestic and external demand, expanded at one of the fastest

rates in the EU. As far as Germany’s economy is concerned, growth

in the first half of the year was driven to a large extent by exports,

which continued to boost the country’s trade surplus. Even though

household consumption increased at a slower-than-expected pace,

its growth should accelerate, given the robust state of the labour

market, including low unemployment rate and wage growth.

Economic growth trends observed in the Scandinavian re-

gion are mixed, with the recovery in Sweden and Denmark

gaining pace and Norway still reeling from the impact of the

slump in oil prices. Supported by growing investment, household

consumption and exports, Sweden’s economy continues to grow at

a rather robust pace — the country’s real GDP increased at annual

rates of 2.8 per cent and 3.3 per cent respectively in the first two

quarters of 2015. Still, the rapid growth in housing prices and the

high level of household debt pose risks to the country’s growth

sustainability. With inflation staying below the official target rate, the

Central Bank of Sweden, or the Riksbank, lowered its repo rate by

0.1 p.p. (to –0.35%) in July and, in October, extended its quantitative

easing programme by SEK 65 billion to a total of SEK 200 billion by

After several years of sluggish economic growth, Russia fell into a recession in 2015.

Chart 3. Russia’s real GDP development

The economies of Lithuania’s important export partners are expanding at moderate paces and the growth is mostly driven by domestic demand.

Chart 4. Development of real GDP of Lithuania’s export partners

After a slight recovery at the beginning of 2015, the fall in prices for commodities, in particular metals and crude oil, re-accelerated in May, which was mainly due to supply glut.

Chart 5. Commodity price indexes

–2

0

2

4

6

2012 2013 2014 2015EstoniaLatviaPolandGermany

Per cent

Source: Eurostat.

40

50

60

70

80

90

100

110

120

2012 2013 2014 2015Food commodities

Metals

Crude oil*

Sources: IMF and Bank of Lithuania calculations. * Average oil prices of WTI, Brent and Dubai Fateh

Index, 2011 = 100

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the end of June 2016. The Danish economy continues to im-

prove,supported by a rise in employment, growth of disposable income,

which boosts consumer spending, as well as by low oil prices and low

interest rates. Meanwhile, Norway’s economic health took a turn for the

worse, mainly due to issues in oil and gas sectors. With business and

consumer expectations faltering, the country witnessed a decline in

investment, an increase in unemployment and a deceleration in wage

growth. As estimated by the IMF, the growth of Norway’s real GDP will

slow down to 0.9 per cent in 2015.

Prices for many key commodities continued to slide in 2015. Af-

ter a slight recovery at the beginning of the year, the fall in commodity

prices re-accelerated in May, mainly due to supply-side factors — in

particular, oversupply and growing inventories. Weakening demand in

China put additional downward pressure on prices for certain commodi-

ties, such as metals. Not surprisingly, the emerging market economies

that are heavily reliant on commodity exports were the most vulnerable

to falls in commodity prices. However, commodity-exporting advanced

economies, e.g. Australia or Canada, also experienced a slowdown.

Some of the commodity prices (e.g. of food) are expected to move into a

recovery mode in 2016. Prices for other key commodities (e.g. crude oil

or metals), however, are projected to remain broadly unchanged or

slightly lower than in 2015.

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II. MONETARY POLICY OF THE EUROSYSTEM

In 2015, the Eurosystem kept its interest rates steady after cut-

ting them twice to record lows in 2014 (in June and October). In

2014, the Governing Council of the ECB lowered the benchmark interest

rate (i.e. the interest rate on the main refinancing operations) to 0.05 per

cent and the interest rate on the marginal lending facility — to 0.30 per

cent. It also set an unconventional (negative) interest rate on the deposit

facility — first, of minus 0.10 per cent (on 11 June 2014), and later, of

minus 0.20 per cent (on 10 October 2014).1 The Eurosystem has been

gradually decreasing its interest rates since 2011 in response to the

stagnant economy and dwindling inflation of the euro area.

The targeted longer-term refinancing operations (TLTROs),

which were launched in 2014, provided credit institutions with

alternative access to long-term credit resources. The TLTROs are

conducted as planned and are scheduled to run until mid-2016.

At the beginning of 2015, the Eurosystem reinforced the ac-

commodative stance of its monetary policy with the adoption of an

expanded asset purchase programme (APP) in January and its

subsequent launch in March. The Eurosystem plans to purchase

EUR 60 billion a month of public and private sector debt securities under

this programme until the end of September 2016 or later if necessary.

The end date of the expanded APP will depend on the success in

achieving the goal of this programme, which is to get inflation back on a

sustainable path consistent with the primary monetary policy objective,

i.e. the inflation rates of below, but close to, 2 per cent over the medium

term. The total scale of the expanded APP, which is scheduled to run for

19 months, will amount to EUR 1.14 trillion, which would increase the

Eurosystem’s balance sheet by 1.5 times. The expanded APP is often

referred to as quantitative easing (QE) of the Eurosystem.2

The Public Sector Purchase Programme (PSPP) is the main

component of the expanded asset purchase programme. From

March to October 2015, the Eurosystem purchased EUR 487.5 billion

worth of debt securities (EUR 60.9 billion per month on average). Public

sector bonds accounted for 81 per cent of those securities, covered

bonds of credit institutions — 17 per cent, and asset-backed securities

— 2 per cent.

The expanded asset purchase programme provides stimulus to

the euro area’s economy via the following main transmission

channels:

1) the signalling channel, by communicating the Eurosystem’s

commitment to maintain a high degree of monetary accommodation for

an extended period of time;

2) the direct impact channel, by driving down the yields on various

maturity bonds purchased under the APP;

3) the (investment) portfolio rebalancing channel, which transmits

the effects of the first two channels to other financial markets. The

stimulus effect of the APP leads investors to rebalance their investment

portfolios by stepping up purchases of debt and equity securities and

other financial assets not included into the scope of the APP;

4) the credit channel, by easing credit standards for loans to cor-

porations and households.

_________________________________

1 For details about the objectives of negative interest rates see the box “Eurosystem’s mone-tary policy instruments and their application in Lithuania” of the Lithuanian Economic Review of June 2015, p. 8. 2 For more information about the expanded APP and its objectives see the box “Eurosys-tem’s monetary policy instruments and their application in Lithuania” of the Lithuanian Eco-nomic Review of June 2015.

The levels of the interest rates of the ECB and EURIBOR are the lowest in the history of the euro area.

Chart 6. ECB interest rates and 6-month EURIBOR

Starting from 2011, the benchmark interest rate of the ECB was lowered gradually in response to stagnant economy and dwindling inflation in the euro area.

Chart 7. ECB’s benchmark interest rate and inflation

The yields on bonds purchased under the expanded APP posted the biggest decline before the launch of bond purchases amid the build-up of market expectations for a future announcement of APP. Later the yields swung between ups and downs driven not only by the APP, but also by changes in forecasts for the euro area economy.

Chart 8. Annual yields on euro area’s government bonds with a residual maturity close to 10 years and issued in national currencies

–1

0

1

2

3

4

5

6

1999 2001 2003 2005 2007 2009 2011 2013 2015

The minimum bid rate in an MRO tender

Deposit facility

Marginal lending facility

6-month EURIBOR

Sources: ECB and Bloomberg.

Per cent

–1

0

1

2

3

4

5

6

1999 2001 2003 2005 2007 2009 2011 2013 2015

The minimum bid rate in an MRO tender

Annual HICP developments

Sources: ECB and Thomson Reuters.

Per cent

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

2014 2015

Per cent

Average in the euro area LithuaniaGermany FranceItaly Spain

Sources: ECB and Bloomberg.

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The direct impact of the expanded APP on the yields of the pur-

chased debt securities is not unequivocal. The impact is watered

down by the fact that the yields of the purchasable bonds were already

low, as was their dispersion, before November 2014, which saw a surge

in market expectations for an imminent launch of the expanded APP by

the Eurosystem. Bond yields were also affected by wavering medium-

term expectations of the euro area’s inflation and economic growth. The

yields climbed higher in the second quarter of 2015 as markets and

analysts revised upwards their forecasts for inflation and GDP growth,

but then again edged lower during the third quarter amid heightened

concerns over a potential slowdown in China and other emerging

economies and its repercussions for the euro area’s economy.

The non-standard monetary policy measures implemented by the

Eurosystem fuelled a decline in lending rates to the real economy

and the narrowing of their dispersion among the euro area count-

ries. Over 2014 and the first nine months of 2015, the weighted average

interest rate on new loans to non-financial undertakings in the euro area

decreased by 0.8 p.p. and the gap between the highest and the lowest

average interest rates across the euro area countries narrowed by the

same margin (0.8 p. p.), as a result of the ECB’s very low interest rates,

the expanded APP and TLTROs. The weighted average interest rate on

new housing loans fell by 0.8 p.p. in the same time period while the

spread between the highest and the lowest average interest rates

across the euro area countries dropped by 0.6 p.p.3

In Lithuania, the average interest rates on new loans to non-

financial undertakings and on new housing loans fell by 1.26 p.p.

and 0.6 p.p., respectively, in the same time period. In addition to the

general downward trend in interest rates on loans in euro, those deve-

lopments were also driven by the adoption of the euro. Nevertheless,

the latter factor should not be taken as decisive, given the strong

expectations of the changeover to the euro in 2015, which built up as

early as in 2014. The average interest rate on loans to Lithuania’s non-

financial undertakings continued to hover at 0.3 to 0.86 p.p. above the

euro area’s weighted average in the abovementioned period since the

country’s companies are usually smaller in size and therefore have a

higher risk profile. In contrast, housing loan interest rates in Lithuania

were lower than the euro area’s weighted average by 0.36 to 0.62 p.p.

during that period. This, above all, was due to the prevalence of housing

loans with variable interest rates pegged to short-term interbank interest

rates (usually 3- or 6-month EURIBOR), which have been at low levels

lately. Meanwhile, calculations of the euro area’s weighted average are

mainly based on loans with interest rates fixed for more than 10 years.

The accommodative monetary policy implemented by the Eurosys-

tem promoted lending to the real economy of the euro area. Based

on the enhanced methodology for adjusting loans for sales and securiti-

sation, which was adopted by the ECB on 21 September 2015, the

annual rate of growth in loans to households moved into positive territo-

ry in November 2014, while the respective rate for loans to non-financial

undertakings followed suit in July 2015 (after three years in the negative

zone). SME surveys in 2015 showed improvements in credit conditions

for those enterprises for the first time since 2009.

Growing divergence in monetary policy cycles between the euro

area and the US set the euro’s exchange rate on a downward path

in the second quarter of 2014, which boosted the euro area’s

export potential. Between mid-2014 and the end of September 2015,

_________________________________

3 To minimise the effects of temporary fluctuations and to filter out the trends, changes are

measured using three-month moving averages for loans to non-financial corporations and

mortgage loans.

The non-standard monetary policy measures imple-mented by the Eurosystem fuelled a decline in interest rates on new housing loans and the narrowing of their dispersion among the euro area countries.

Chart 9. Average interest rate on MFIs’ new housing loans

The non-standard monetary policy measures imple-mented by the Eurosystem fuelled a decline in interest rates on new loans to non-financial undertakings and the narrowing of their dispersion among the euro area countries.

Chart 10. Average interest rate on MFIs’ new loans to non-

financial undertakings

1

2

3

4

5

6

2013 12 2014 06 2014 12 2015 06

Per cent

Dispersion of interest rates across countries of the euro area

In the euro area

In Lithuania

Sources: ECB and Bank of Lithuania calculations.

1

2

3

4

5

6

2013 12 2014 06 2014 12 2015 06

Per cent

Dispersion of interest rates across countries of the euro area

In the euro area

In Lithuania

Sources: ECB and Bank of Lithuania calculations.

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the euro dropped by 22 per cent against the US dollar and by 19 per

cent against the Chinese yuan. The real effective exchange rate of the

euro vis-à-vis the currencies of 38 of the euro area’s main trading part-

ners, based on consumer price indexes, fell by 10 per cent between the

first quarter of 2014 and the end of the third quarter of 2015. The real ef-

fective exchange rate of the Lithuanian national currency (until early

2015, the litas, and later, the euro) decreased by 2 per cent in the same

time period.

As a member of the Eurosystem since the beginning of 2015,

the Bank of Lithuania implements standard and non-standard mo-

netary policy operations. Since the beginning of the year, the Bank of

Lithuania applies to the credit institutions operating in Lithuania the mi-

nimal reserve requirement ratio valid in the Eurosystem, also offering to

the credit institutions of Lithuania MRO, LTRO (including TLTRO), as

well as marginal lending and deposit facilities. Since there is no market

of either covered bonds or asset-backed securities in Lithuania, the

Bank of Lithuania does not participate in the purchase programmes of

those assets, but it takes part in the implementation process of the pub-

lic securities purchase programme. The Bank of Lithuania also buys

bonds issued by the European supranational institutions that are eligible

for purchase under the APP, because the amount of debt securities is-

sued by the government of Lithuania is not sufficient to fulfill Lithuania’s

requirement for bond purchases, drawn according to its capital key.

The Bank of Lithuania forecasts that the extended asset pur-

chase programme will impact the export sector in Lithuania’s eco-

nomy the most, since foreign demand for Lithuanian goods and

services will increase in the euro area, adding that the euro foreign

exchange rate will weaken. According to the Bank of Lithuania, the

expanded APP can increase the growth of the country’s real GDP by

around 0.2 per cent in 2015 and by 0.1 per cent both in 2016 and in

2017. The programme will be the most favourable for the export sector

of Lithuania’s economy due to higher foreign demand for Lithuanian

exports in the euro area and due to the lower euro foreign exchange ra-

te. The impact on Lithuania’s economy via interest rates and the credit

channel will also be positive, but smaller, because yields on public debt

securities and lending interest rates had already been low before the

launch of the programme, lending margins are rather stable, and credi-

ting to the real economy recovers slowly.

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–10

–5

0

5

10

15

20

25

–10

–5

0

5

10

15

20

25

2013 2014 2015

Russia

EU countries

Other countries

Exports of services (rh scale)

Source: Bank of Lithuania and Bank of Lithuania calculations.

Percentage points Per cent, annual change

–9

–6

–3

0

3

6

9

–9

–6

–3

0

3

6

9

2012 2013 2014 2015

Final consumption expenditure

Domestic investment (excl. inventory changes)

Net exports

Changes in inventories

GDP (rh scale)

Percentage points

Sources: Statistics Lithuania and Bank of Lithuania calculations.


III. REAL SECTOR

The growth of Lithuania’s economy has decelerated, weighed

down by the external environment. Exports to Russia have plumme-

ted as a result of the trade restrictions imposed by it and the country’s

economic deterioration. With re-exports accounting for the bulk of

Russia-bound exports of goods, this sharp fall in exports has a substan-

tial downward effect on performance of Lithuania’s providers of trans-

port services. Despite the unfavourable external environment, domestic

demand has thus far remained resilient, with both household consump-

tion and domestic investment growing at a healthy pace. Still, the

slowdown in Lithuania’s economic expansion has brought back a

negative output gap, which had almost closed in 2014.

The grave state of Russia’s economy, coupled with the trade

restrictions enforced by it, has dealt the biggest blow to the trans-

port and storage activity. In 2014, the exports of transport and stora-

ge services to Russia accounted for more than one-tenth of the total

output of services provided by the Lithuanian transport and storage

sector, therefore, having decreased by more than a third, it had a

significant impact on the development of this economic activity. Provi-

ders of transport and storage services are gradually refocusing their

operations towards other markets, such as France, the United Kingdom

or Denmark, which largely offsets the losses sustained in Russia. It

should be noted that the losses suffered by the providers of storage

services are somewhat higher due to lesser chances to refocus this

activity to other markets. For example, in contrast to transport compa-

nies with highly mobile productive capital (such as trucks), the producti-

ve capital of storage undertakings is extremely inflexible and highly

dependent on a country’s external trade trends.

The situation in Russia also has repercussions on manufactu-

ring, which, however, are limited. Russia is not a major market for the

exports of Lithuanian manufacturing. In 2014, exports to Russia ac-

counted for less than 3 per cent of manufacturing output whereas in

January through September 2015, this share decreased further, to

1.5 per cent. As far as the effects of Russia’s trade restrictions on the

manufacturing industry are concerned, the only manufacturing bran-

ches that are worth mentioning are related to the production of dairy

and meat products, which have the strongest ties to Russia. Manufactu-

rers of dairy products have thus far failed to make up for the fall in

revenue triggered by Russia’s trade restrictions. Manufacturers of meat

products are faring better, as they offset the lost revenue by stepping

up domestic sales and by increasing exports to other external markets.

The development of manufacturing is also constrained by rather subdu-

ed growth of demand in the country’s key trading partners. However,

sales revenue in these markets is growing at a faster pace compared to

demand, which leads to increases in Lithuania’s export market shares

in these economies. A positive contribution to the development of

manufacturing is also made by the recovery of the manufacture of

refined petroleum products; it is driven by higher refining margins.

Thus far, the weaker external environment has mainly affected

corporate operating surplus and the companies have been wary of

scaling down compensation for employees. Such behaviour by

Lithuania’s companies reflects their expectations that the economic

slowdown will not be long-lived. Therefore, they do not make any

substantial adjustments to their development roadmaps and are in no

rush to reduce the headcount or revise the existing wage policy. This

leads to labour market developments that are favourable for house-

holds. Improvements in the labour market fuel growth in household

income and, at the same time, support the incentives for households to

consume. This, coupled with falling prices and ultra-low loan interest

The slowdown of Lithuania’s economic growth is caused by the unfavourable external environment. However, this factor has thus far left domestic demand unaffected.

Chart 11. Contributions to the development of real GDP by expenditure approach

Transport and storage activity suffers losses due to the grave state of Russia’s economy and trade restrictions slapped by it. However, much of these losses are recouped by way of redirecting more services to other markets.

Chart 12. Exports of services broken down by markets (at current prices)

The effect of factors associated with Russia on manu-facturing is limited since Russia is not a major market for the exports of activity’s products.

Chart 13. Manufacturing sales (at constant prices)

–15

–12

–9

–6

–3

0

3

6

9

12

15

–15

–12

–9

–6

–3

0

3

6

9

12

15

2012 2013 2014 2015

Sales of refined petroleum products

Sales of other products in domestic markets

Sales of other products in Ukraine

Sales of other products in CIS, excl. Russia

Sales of other products in other foreign markets, excl. CIS and Ukraine

Sales of other products (non-embargo ) in Russia

Sales of other products (embargo) in Russia

Manufacturing sales (rh scale)

Percentage points

Sources: Statistics Lithuania and Bank of Lithuania calculations..


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–10

0

10

20

30

40

–20

–10

0

10

20

30

40

2012 2013 2014 2015

Residential buildings

Non-residential buildings

Civil engineering structures

All construction, construction work data (rh scale)

Value added of construction activity, National Acounts data (rh scale)



–2

0

2

4

6

8

10

–2

0

2

4

6

8

10

2012 2013 2014 2015

Compensation of employees

Operating surplus and mixed income

Consumption of fixed capital

Taxes on production and imports

Subsidies

GDP (rh scale)



rates, results in active household consumption and strong retail trade

activity.

The construction industry acted as a major catalyst for the

growth of economic activity for a while. However, activity in this

sector is expected to cool down in the latter half of 2015. Construc-

tion output, which develops very similarly to the value added of const-

ruction, fell by an annual 4 per cent in the third quarter of 2015, driven

down in particular by steep declines in engineering and non-residential

construction, which were triggered inter alia by the implementation of

economic projects of national significance. On the other hand, residen-

tial construction continues to grow at a solid pace. Such developments

in this segment are fuelled by a number of factors, e.g. the improved

health of the economy, the growth of household income and improve-

ments in household financial health, as well as advantageous borrowing

terms offered by banks. Most likely, the recent upswing in home const-

ruction has also been driven by the still large (as of 2014) backlog of

ready construction projects, which were put on hold in the second half

of 2008 due to the rise of the financial crisis. Residential construction

developers might have launched these projects in response to an

increase in demand. However, the existing supply of new homes, which

is relatively plentiful, may hamper further growth of residential construc-

tion.

Thus far, the weaker external environment has mainly affected corporate operating surplus and the compa-nies have been wary of scaling down compensation for employees.

Chart 14. Contributions to the development of nominal GDP by income approach

The construction activity acted as a major catalyst for the growth of economic activity for a while. However, activity in this activity is expected to cool down in the latter half of 2015.

Chart 15. Construction output and construction value added (at constant prices)

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IV. LABOUR MARKET

The number of people in the labour force, i.e. both employed

and unemployed workers, is decreasing. In the first half of 2015, the

labour force shrank by 0.5 per cent as compared to the period in the

previous year. This is driven by two trends: firstly, a decrease in the

working-age population and, secondly, decelerated growth in the la-

bour force participation rate, i.e. the share of the population participat-

ing in the labour market. The decrease in the working-age population is

led by substantial emigration, high mortality and the declining number

of youths entering working age. Fast growth in the labour force partici-

pation rate, which was observed in 2014 due to the social support re-

form, the introduction of vouchers for the supply of agricultural services

and other factors, slowed down substantially in the first half of 2015. Its

only support came from the growth in the participation rate of older

workers, which was driven inter alia by the increase in the retirement

age.

The impact of Russia’s economic deterioration on employment

in Lithuania is limited and is offset by more favourable effects

from non-tradable economic activities. In the first half of 2015, em-

ployment increased by 1.9 per cent year-on-year and the number of

employees grew at a similar pace. Russia’s economic deterioration

had the biggest effect on the number of jobs in transport and trade

companies4: the growth of jobs in these activities decelerated to its

slowest pace since the onset of economic recovery.5 The knock-on

effect from Russia’s economic woes on hiring in manufacturing was

rather marginal and the pace of job growth in this sector was almost as

fast as before. Meanwhile, the economic activities with more limited

ties to Russia showed an acceleration in job growth, which was mainly

driven by the services sector targeting domestic demand. A pickup in

job creation was also observed in the construction sector, which, at the

time, was increasing its output. Still, in mid-2015 the hiring expecta-

tions of companies across all sectors were lower than in 2014; also, a

number of construction confidence indicators worsened.

The unemployment rate continues its downward path and is

close to the estimates of the natural unemployment rate. In the

first half of 2015, the jobless rate fell by 2.2 p.p year on year, to stand

at 9.7 per cent. The unemployment rate among unskilled workers,

however, was much higher, with approximately one-fourth of them out

of work and engaged in active job searching. A more notable decrease

in the number of unskilled unemployed workers has only been ob-

served in the past several quarters owing to the strengthening of active

labour market policy measures. The unemployment of such workers is

likely to be structural as the number of these unemployed persons has

hardly decreased despite rather robust growth of the economy since

2011. Even though unskilled workers account for only around one-

tenth of the labour force, they push the overall unemployment rate

upward rather substantially, by approximately 2 p.p. The unemploy-

ment rate for other workers was lower and in the first half of 2015

stood at 7.7 per cent.

The decrease in the unemployment rate, coupled with a rather

healthy growth in employment, promotes relatively rapid wage

growth. In the first half of 2015, annual wage growth reached 4.5 per

cent. As compared to the previous half-year, however, wage growth

decelerated by 0.4 p.p. Most likely, this slowdown has nothing to do

_________________________________

4 Of all trade activities, the number of wholesale jobs was affected the most. However, Sta-

tistics Lithuania does not differentiate this activity from other types of trade activities in its statistics. 5 The employee-number data is not directly comparable to the job-number data, yet both

sources show hiring developments. This section uses the data on the number of jobs, which provide a more precise picture of hiring changes by economic activity

Favourable development of economic activities related to domestic demand offsets the fallout from Russia’s economic plight on employment.

Chart 16. Developments in jobs

The stubbornly high unskilled unemployment rate signals structural problems of the labour market.

Chart 17. Unemployment rate (seasonally adjusted)

Companies experience slightly more difficulties in finding adequately skilled workers.

Chart 18. Labour supply indicators (seasonally adjusted)

0

1

2

3

4

0

1

2

3

4

2012 2013 2014 2015

Other activities

Trade and transport

Number of jobs (rh scale)

Sources: Statistics Lithuania and Bank of Lithuania calcualtions.


0

5

10

15

20

25

30

35

2002 2004 2006 2008 2010 2012 2014

Total

Unskilled persons

Other persons

Per cent


0

10

20

30

40

50

60

0

10

20

30

40

50

60

2004 2006 2008 2010 2012 2014

Number of unemployed persons per vacancy

Share of companies facing labour shortages (rh scale)

Unemployed persons


Per cent

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with the economic deterioration in Russia, as the wage growth decel-

eration in the economic activities that are most closely related to Rus-

sia was only slightly higher than the slowdown observed in the whole

economy. To some extent, the deceleration in wage growth can be at-

tributed to a technical factor, i.e. unfavourable developments in the

number of paid working hours per employee.6 In general, wage

growth, which is quite solid, is driven by the rather healthy state of the

labour market, which, among other things, is reflected in substantial

declines in the unemployment rates for certain occupations. In these

circumstances, companies experience slightly more difficulties in find-

ing adequately skilled workers, as the number of unemployed per va-

cancy continues to decrease steadily. Still, the worker shortage now is

much smaller than it was during the economic upswing (in 2006 to

2007). In recent years, a substantial increase in labour shortage has

only been observed in the retail sector.

_________________________________

6 For some wage-earners, the monthly wage depends on the number of working days or the

number of hours worked in a respective month.

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5

10

15

20

25

30

–15

–10

–5

0

5

10

15

20

25

30

2012 2013 2014 2015

Baltic states

EU countries, excl. Baltic states

CIS countries

Other countries

Exports of goods (rh scale)

Exports of goods, excl. mineral products (rh scale)


Percentage points Percent, annual change

V. EXTERNAL SECTOR7

The worsening of Lithuania’s export performance in the first

three quarters of this year from the year-earlier period was due to

a sharp fall in exports to Russia. However, exports to the EU,

Lithuania’s key foreign trade market, remained on a consistent

growth path. The nominal value of exports shrank by slightly more

than 5 per cent, which mainly was a result of developments in trade

with Russia. In particular, Lithuania’s exports to Russia dropped by

more than 40 percent year-on-year against the backdrop of weaker

demand in that country and its trade restrictions, which were kept in

place in the period under review. Excluding the effects related to

Russia’s economy, the rate of export growth comes in at slightly more

than 4 per cent. The growth of exports continues to be supported by

the competitiveness of the country’s wood and furniture manufacturing

and the resulting continuous growth in sales of this activity’s output in

the EU countries. This year, remarkable growth has been observed in

particular in the exports of wood products to Sweden, the United

Kingdom and the Netherlands. The order backlog of the country’s

furniture manufacturers has been growing for a while now and the

producers of furniture continue to invest in upgrading production

capacity. The Lithuanian chemical industry’s sales abroad, which

came nearly to a standstill in the middle of the year, have returned to

the growth path.

A spike in the crude oil refining margins has led to improve-

ments in the market for mineral products, including an increase in

refining capacity utilisation of AB ORLEN Lietuva and the growth of its

sales volumes. AB ORLEN Lietuva generated a profit of EUR 172

million in the first three quarters of this year, which was one of the best

results recorded by the refinery since its acquisition by Poland’s PKN

Orlen back in 2006. Recently, the refining margins have been higher

than the average for 2008–2015; therefore, the mineral product export

performance is expected to remain robust in the months ahead.

Grain exports should increase in the near future, after recor-

ding a year-on-year slump of nearly one-fifth in the first half of 2015.

The price is an important factor in farmers’ decisions when selling

grain; therefore the deterioration in performance, observed in the first

half-year, may relate closely to the fact that grain exporters sold their

volumes at the end of 2014 at the then higher prices. Grain harvest is

expected to reach a record level this year, yet the grain quality will

decline. This is driving down grain export prices, thus the value of

grain exports this year is unlikely to significantly exceed last year’s

amount.

The development of re-exports has mostly been affected by

the economic deterioration in Russia and the embargo that it

imposed. Acceleration in the fall of re-exports in the third quarter was

mainly due to the base effect stemming from a temporary increase in

the re-exports of machinery and equipment to Russia in the middle of

2014. The rough economic situation in Russia has had significant

repercussions for other CIS countries with close economic and finan-

cial ties to Russia and has also contributed substantially to the

weakening of their currencies. The depreciation of the currencies of

the main importers of vehicles from Lithuania (Russia, Belarus and

Kazakhstan) eroded the demand for these means of transport and led

to a prolonged decrease in vehicle re-exports.

_________________________________

7 This section reviews nominal foreign trade indicators.

Extremely unfavourable developments in trade with Russia undermine export performance.

Chart 19. Lithuania’s exports broken down by country groups (three-month moving sums)

An upturn in oil refining margins led to an increase in refining capacity utilisation of AB ORLEN Lietuva and

the growth of its sales volumes.

Chart 20. Evolution of the refining margin of AB ORLEN Lietuva

The current account balance has fallen to three-year lows, mainly due to unfavourable export develop-ments and a substantial increase in imports.

Chart 21. Components of the current account balance

0

2

4

6

8

10

12

2008 2009 2010 2011 2012 2013 2014 2015

AB ORLEN Lietuva oil refining margins

Long-term average

EUR per barrel

Sources: AB ORLEN Lietuva and Bank of Lithuania calculations.

–15

–12

–9

–6

–3

0

3

6

9

12

2012 2013 2014 2015

Goods balanceServices balancePrimary income balanceSecondary incomeCurrent account balance

Sources: Statistics Lithuania, Bank of Lithuania and Bank of Lithuania calculations.

Percentage of GDP

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Unfavourable export developments and a substantial inc-

rease in imports contributed to the worsening of the current

account balance, which fell to a three-year low of –5.0 per cent

of GDP in the first half of this year. The primary income balance

also showed unusual deterioration due to an increase in dividend

outflow from Lithuania. In addition, remittances to Lithuania from

abroad fell by nearly one-fourth in the first half of 2015, year on

year, and, therefore, played a less important role in improving the

current account balance. Presumably, the decline in remittances

was triggered by factors related to the currency changeover, i.e.

that emigrants cut down on their investment in property in Lithua-

nia as the new currency was introduced.

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–3

–2

–1

0

1

2

3

4

–3

–2

–1

0

1

2

3

4

2012 2013 2014 2015

Administered pricesPrices of food incl. beverages and tobaccoPrices of fuels and lubricantsPrices of servicesPrices of industrial goodsAnnual core inflation* (rh scale)Annual inflation (rh scale)

Percentage points Per cent

Sources: Statistics Lithuania and Bank of Lithuania calculations. * Change in HICP excl. food, fuels and lubricants, and administered prices.

0

15

30

45

60

75

90

105

120

135

–60

–50

–40

–30

–20

–10

0

10

20

30

2012 2013 2014 2015

Oil price, USD (rh scale)

Oil price, EUR

Fuel prices in Lithuania


Sources: Bloomberg, Statistics Lithuania and Bank of Lithuania calculations.

–8 –6 –4 –2

02468

1012

2012 2013 2014 2015

Administered prices

Food incl. beverages and tobacco

Services

Industrial goods



VI. PRICES AND COSTS

Recently, consumer prices have been well below the levels a

year ago, i.e. the annual inflation rate was negative. In January–

October 2015, the annual inflation rate averaged −0.7 per cent. Current

negative inflation is the result of a mix of factors dampening the growth

of prices or pushing them down. These factors include inter alia low

inflation in the euro area, which impacts prices in Lithuania through

imports, and a slump in global commodity prices.

Nevertheless, this spell of negative inflation is predominantly

driven by one particular factor, i.e. the developments in prices for

crude oil in global markets. Brent crude oil prices plummeted by

nearly one-half in USD terms, or by more than one-third in EUR terms

in January–October 2015, on a year-on-year basis. This substantial

difference in the dynamics of oil prices expressed in these currencies

can be explained by changes in the exchange rate of the euro versus

the US dollar: from spring 2014 the exchanged rate weakened for a

long time. The slump in oil prices is attributed to the supply glut and the

subdued growth of demand, also, to the decisions of the Organization

of the Petroleum Exporting Countries (OPEC) not to cut output.

As a direct consequence, the fall in oil prices has led to a

slump in fuel prices in Lithuania. Fuel carries a substantial weight in

the consumption basket (more than 7%), which implies that the strong

dynamics of fuel prices have a profound effect on the headline inflation

and its developments. The annual fall in fuel prices reached nearly

20 per cent in January through April 2015, then halved in May–July

and later, in August–October, re-accelerated, approaching the pace

observed early in the year. The evolution of headline inflation followed

a very similar path, i.e. the large fall in prices at the beginning of the

year decelerated in the middle of the year, coming close to zero, only

to gather speed once again afterwards. In general, inflation driven into

negative territory by falling fuel prices in Lithuania is not much of an

outlier in the EU context: the decline in prices for oil and other energy

commodities brought down energy prices for consumers in all EU

Member States and was the main cause of negative inflation in some

of these markets.

Aside from the main factor, i.e. fuel, other components of the

consumption basket also contributed to the negative inflation, yet

their impact was much less pronounced. Administered prices

followed a steady downward trend, in particular thanks to the prices for

heating energy, which kept falling, dragged down by the slumping

prices for natural gas. The data from the National Commission for

Energy Control and Prices shows that natural gas prices in August

2015 were 16 per cent below the level recorded in the same month of

2014 (August gas prices were used in the calculations of heating

prices for October). The dynamics of prices for industrial goods or food

were less clear-cut as compared to fuel or administered prices and

their annual changes varied between positive and negative values in

the first ten months of 2015. To summarise, it may be said that prices

for industrial goods dropped in the first half of the year while food

prices decreased in the middle of the year.

Prices for services are the only category to resist the

downward pressure. Quite the opposite, they are increasing at a

rather fast pace, mostly due to growing domestic demand, which is

supported by improvements in the labour market, i.e. the growth in

employment and wages. Still, the labour-cost pressure is not very high.

Leaving the macroeconomic factors aside, a slight additional contribu-

tion to the increase in prices for services possibly stemmed from the

adoption of the euro.

In 2015, annual inflation developments were mostly driven by fuel prices. They tumbled rapidly early in the year, the pace of decline slowed down in the middle of the year, only to accelerate at the end of summer.

Chart 22. Contributions to annual HICP inflation

Fuel prices were driven down by falling oil prices: in January through October, the price of crude oil in EUR plummeted by more than one-third year-on-year.

Chart 23. Developments of Brent crude oil prices and fuel prices in Lithuania

Price falls are not limited to fuel. Administered prices remain on a steady downward trend. Prices for industrial goods moved down in the first half of the year and food prices went on a downward trajectory in the middle of the year.

Chart 24. Price developments in individual categories of the consumption basket (excluding fuel)

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Price trends are expected to remain favourable for consumers.

Most of the consumer price statistics for 2015 have already been

released; therefore, it is obvious that the average annual inflation rate in

2015 will be negative. In 2016, consumer prices will increase somewhat

due to the receding downward effect from energy prices, which have

been the main contributor to negative inflation rates this year, as well as

due to a pickup in non-energy inflation.

The downward movement of energy prices is expected to slow

down substantially next year as compared to 2015. This view is

corroborated by oil price forecasts issued by international institutions.

For example, the forecasts published by the IMF in October 2015 and

by the European Commission in the following month show that USD-

denominated price of oil in 2016 will be only slightly lower than in 2015.

Prices for food, including beverages and tobacco, are projec-

ted to increase in 2016, after staying broadly unchanged over 2015.

Their rebound, however, should not be strong, as it will be suppressed

by the slow growth in prices for food commodities in global markets.

According to the forecasts published in October by the research and

analysis firm the Economist Intelligence Unit, the global prices for food,

feedstuff and beverages in USD will grow by 3 per cent in 2016, compa-

red to 2015. Even if such forecasts come true, these prices will stay

relatively low, given their substantial falls, recorded in recent years. The

Economist Intelligence Unit estimates that these prices will drop by

18 per cent in 2015 alone (the fall in prices expressed in EUR is less

pronounced due to the USD appreciation). In 2016, food prices in

Lithuania will be affected by the increase in excise duties, but its effect

on headline inflation will be marginal. Similarly to this year, the excise

duty on cigarettes (in order to reach, in increments, the minimum EU

rate) will be increased in March, as will be the duty on alcoholic bevera-

ges.

The growth of other — non-food and non-energy — prices is

expected to remain moderate, since it will be dampened by several

factors. Firstly, pressure from domestic demand in Lithuania should be

limited, as the growth of unit labour costs is expected to not be fast and

slower than in 2015. Secondly, the outlook for the euro area anticipates

low inflation ahead. Various institutions expect the inflation rate to be

close to 1 per cent in 2016. Forecasts published by the IMF (in October)

and by the European Commission (in November) expect a 1.0 per cent

increase in prices in 2016. The latest results of the ECB Survey of

Professional Forecasters, which were published in the final quarter of

2015, show the same expectations.

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VII. FINANCING OF THE ECONOMY8

Improvements in corporate financial health and the high level of

consumer confidence in economic development have taken the

demand for loans and the loan portfolio of monetary financial

institutions on a gradual growth path. The annual pace of decrease in

the portfolio of loans issued by the MFIs slowed down in the first half of

2015. In the third quarter the decline reversed to moderate growth and

thus the loan portfolio exceeded its previous level by 1.3 per cent at the

end of the third quarter of this year.

A strong contribution to the growth of the loan portfolio came

from lending to households. The portfolio of loans to households

increased by 3 per cent at the end of the third quarter of 2015, year on

year, driven by loans for house purchase and loans for consumption. The

growth of employment and wages observed this year has increased

consumer confidence in the economic outlook and led to gains in house-

hold financial health and to a rise in the proportion of households plan-

ning to purchase durable goods (such as a home or a car). Such

purchases contribute to the growth of the banks’ loan portfolio, as they

are often financed with borrowed money. Moreover, the acceleration in

the growth of housing loans this year might have been additionally

induced by the announcement, in early 2015, of the revised Responsible

Lending Regulations, which came into effect on 1 November 2015.

Households might have sought to shift their house purchase forward

amid anticipation that the amendments to those regulations would make

it more difficult to qualify for a loan for house purchase.

Loans to businesses and to the general government sector,

which showed a decrease, had a chilling effect on the loan portfolio.

Nevertheless, the pace of the decline in loans to businesses has

recently slowed down. The portfolio of loans to non-financial undertak-

ings shrank by 1 per cent at the end of the third quarter of 2015, mainly

due to a drop in loans to real estate operations, construction, and manu-

facturing industry. The portfolios of loans to these economic activities

decreased by 9.7 per cent, 10.6 per cent and 2.7 per cent, respectively,

at the end of the third quarter of 2015, year on year, seemingly due to the

availability of other sources of funding, such as earnings retained from

previous periods. The highest growth in the loan portfolio came from

loans to energy supply operations as well as loans to wholesale and retail

trade. The portfolios of loans to these activities rose by 13.4 per cent and

9.4 per cent, respectively. Growth in the portfolio of loans to the energy

supply sector was probably induced inter alia by ongoing energy infra-

structure construction and upgrading projects, which require substantial

financial resources. In regards to the loan portfolio of the wholesale and

retail trade sector, its growth might have been driven by increasing

consumption. Still, faster growth in the equity of trade enterprises re-

duced the level of financial leverage in the sector.

The average interest rate on loans decreased this year, but

changes in other lending conditions were minimal. The variable

interest rate on house loans shrank by 0.4 p.p. to an average of 1.7 per

cent in the first–third quarters, compared to the same period a year

earlier. The average cost of loans to businesses went down by 0.2 p.p.

while the average variable interest rate on loans of up to EUR 1 million

stood at 3.1 per cent. The general decline in the interest rate was mainly

due to a fall in interbank interest rates and also, specifically in the case of

house loans, due to a slight decrease in the banks’ interest margin. If the

_________________________________

8 The data used in this section to evaluate loans includes the data for the MFIs, as provided by

the Statistics Department of the Economics and Financial Stability Service of the Bank of Lithuania and adjusted to take account of bankruptcies and mergers in the sector concerned (for more details see Annex 2 of the Lithuanian Economic Review, December 2014). This data may differ from the data collected from banks for supervisory purposes.

MFIs’ loan portfolio has started to grow.

Chart 25. Contribution to changes in MFIs’ loan portfolio

Loans to households and businesses are getting cheaper.

Chart 26. Interest rates

The decline in loans to non-financial corporations is slowing down.

Chart 27. Loans to businesses, broken down by economic activity

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–2

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0

1

2

3

4

5

–5

–4

–3

–2

–1

0

1

2

3

4

5

2012 2013 2014 2015

HouseholdsNon-financial corporationsGovernmentFinancial intermediariesMFIs' loan portfolio (rh scale)


Sources: Bank of Lithuania and Bank of Lithuania calculations.

–1

0

1

2

3

4

5

6

2012 2013 2014 2015

Variable interest rate on house loans

Variable interest rate on business loans of up to EUR 1 mil.

3 month EURIBOR

Per cent

Sources: ECB and Bank of Lithuania.

–10

–5

0

5

–10

–5

0

5

2013 2014 2015ManufacturingEnergy supplyReal estate activitiesTradeConstructionTransportation and storageRemaining activitiesLoans to business (rh scale)


Sources: Bank of Lithuania and Bank of Lithuania calculations.

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economy performs in line with its growth forecasts, the loan portfolio is

likely to continue to increase, unless growth is derailed by new major

factors contributing to uncertainty. This forecast is underpinned by ever-

increasing equipment investment requirements, working capital require-

ments and intended purchases of consumer durables by households.

Some of these requirements may be financed with borrowed money,

given the attractiveness of the lending conditions. Moreover, the growth

of the portfolio may also be induced by heightened inter-bank competition

for profits, which have decreased due to the loss of commission income

after the adoption of the euro.

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VIII. GENERAL GOVERNMENT FINANCE

The shape of general government finances continued to improve

in the first half of 2015. Year on year, the ratio of general government

deficit to the four-quarter GDP decreased by 1.6 p.p. However, the

improvement of the general government balance in the first half of 2014

was significantly affected by one-off factors, such as extra revenue

generated due to reclassification of the deposit and investment insurance

company VĮ Indėlių ir Investicijų Draudimas into the general government

sector and pension compensation expenditure. Hence the improvement in

the general government balance in the first half of 2015 comes in as less

significant when adjusted for one-off factors. The breakdown by institu-

tional sectors shows that the main positive contributions to the general

government balance came from a lower deficit of social security funds and

a surplus in the central government balance, also, to a lesser extent, from

an improvement in the local government balance, which, however, was

less substantial. The balance of social security funds improved due to fast

growth in social security contributions and a decline in total expenditure

due to a fall in capital transfers. Improvement of the central government

balance was the result of an increase in revenue from taxes and current

transfers. As regards the local government balance, improvements mainly

came from over-the-target revenue collection against the backdrop of

better-faring labour and property markets, as well as administrative

changes (i.e. the increase in the share of personal income tax allocated to

municipalities).

The growth of general government revenue in the first half of the

year was mostly driven by elevated domestic demand and one-off

factors. The general government revenue-to-GDP ratio rose to 34.9 per

cent in the first half of 2015, from 34.1 per cent at the end of 2014. In

nominal terms, general government revenue increased by approximately

5 per cent in the first half of 2015 year-on-year. However, this increase

masked differences in the performance of individual revenue components.

The continued growth of wages and employment drove up general gov-

ernment revenue from social contributions and direct taxes. Improvements

in the labour market spurred continuing growth in household consumption

expenditure, which pushed up general government revenue from indirect

taxes. The growth of indirect tax revenue was also fuelled by one-off

factors, such as the increase in excise duties on processed tobacco and

alcoholic beverages, effected on 1 March 2015.

The positive effect from the growth of domestic demand on gen-

eral government revenue was offset by a one-fourth decrease in

general government revenue from capital transfers, which was a

result of the high comparative base of the previous year. In particular,

general government revenue from the operations of VĮ Indėlių ir Investicijų

Draudimas fell to approximately EUR 65 million in the first half of 2015,

from approximately EUR 160 million in the same period of 2014. However,

the fall in general government revenue from capital transfers was cush-

ioned somewhat by capital revenue generated in the second quarter of

2015 thanks to the law awarding partial compensation for past wage cuts

to civil servants,9 which was adopted by the Seimas of the Republic of

Lithuania in that period. As a result of this piece of legislation, the income

tax and social contributions related to wage compensation were recorded

as general government revenue.

The increase in personal income tax revenue observed in the first half of 2015 was much stronger than could have been expected,

_________________________________ 9 The Law of the Republic of Lithuania on partial compensation of wages (salaries), which were

reduced disproportionately due to the economic crisis, to persons who are paid for work from

the general government or municipal budgets, No XII-1927, 30 June 2015.

General government deficit narrowed substantially in the first half of 2015, year on year, mainly due to the central government balance moving into surplus.

Chart 28. Contributions to the growth of general government balance broken down by subsectors

Revenue mostly related to increasing domestic demand as well as one-off factors were the main drivers of general government revenue growth in the first half of the year.

Chart 29. Contributions to the growth of general government revenue

Faster growth of personal income tax revenue, compared to growth of wages and salaries, is mostly due to increase in non-labour income.

Chart 30. Annual changes of personal income tax, social security contributions and theoretical tax base

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2

4

2012 2013 2014 2015

Local government balance

Balance of social security funds

Central government balance

General government balance

Percentage of GDP (4-quarter moving sum)


–5

0

5

10

15

20

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0

5

10

15

20

2012 2013 2014 2015

Indirect taxesSocial contributionsTaxes on income, wealth, etc.Non-tax revenueTotal revenue (rh scale)

Percentage points



-2

0

2

4

6

8

10

12

14

2012 2013 2014 2015

Wages and salaries

Personal income tax

Social security contributions


Per cent, annual change (4-quarter moving sum)

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based on the year-on-year change in the theoretical base of the tax,

i.e. wages and salaries. Data from the State Tax Inspectorate shows

that the acceleration in personal income tax revenue growth in the first

half of 2015 was driven by a rise of one-sixth in non-labour income, which

accounted for approximately half of the total increase in personal income

tax revenue. The divergence in annual changes of the tax base and

personal income tax revenue is inter alia due to differences in methodol-

ogy as wages and salaries are recorded on an accrual basis and person-

al income tax revenue — on a cash flow basis.

The substantial decrease in general government spending due

to high comparative base was in fact inhibited by expenditure on

the compensation of wages to civil servants and an uptick in in-

vestment. The general government expenditure-to-GDP ratio narrowed

to 34.6 per cent in the first half of 2015, from 35.4 per cent at the end of

2014. In nominal terms, spending decreased by 0.6 per cent over the

year, mostly due to expenditure on capital transfers, which fell by half.

This decrease in capital transfers can mainly be explained by a high

comparative base, given the payments made by VĮ Indėlių ir Investicijų

Draudimas and money spent on pension compensation in the first half of

2014. The law providing partial compensation of wages to civil servants,

which was adopted by the Seimas of the Republic of Lithuania in June

2015, prevented an even bigger slump in capital transfers in the first half

of the year. Actual payments are scheduled for 2016–2020. However, the

whole amount had to be recorded at the time of incurrence of the respec-

tive liability, based on accrual principle of national accounts. As regards

overall expenditure, the biggest upward pressure in the first half of 2015

came from an increase in expenditure on investment, which was mainly

due to a rise in investment spending of VĮ Turto Bankas and higher state

budget appropriations for vehicle acquisition.

Even though general government investment now follows an

upward path, the proportion of funds earmarked for public invest-

ment in Lithuania is relatively lower than in the other two Baltic

countries. Eurostat data shows that in 2014, Lithuania’s general gov-

ernment investment as a share of GDP was slightly higher than the EU or

the euro area average. Nevertheless, that ratio was one of the lowest

among new EU members (the ratio was lower only in Cyprus). The

analysis of investment as a share of general government expenditure

leads to broadly the same conclusion: general government investment in

Lithuania was higher than average in the EU or the euro area but lower

than in seven (including Poland, Latvia and Estonia) out of 13 new EU

members. The evaluation of the economic recovery period, i.e. the period

between 2010 and 2013, shows a rise in the role of general government

investment in Estonia and Latvia, as evidenced by increases in the

nominal value of general government investment (by one-third in Estonia

and by one-fifth in Latvia between 2010 and 2013) and in the share of

investment in the total general government expenditure. In particular,

Estonia stepped up investment in general services provided to the public

and in the economy, whereas Latvia scaled up its investment in educa-

tion, healthcare and social security. As far as Lithuania and Poland are

concerned, the nominal amount of general government investment fell by

more than one-tenth in each of these countries in the period of 2010–

2013, which also saw a decrease in the shares of investment in the total

general government expenditure. Investment in Poland was scaled down

across the board in relative terms, but, in particular, in the general

services to the public and in the economy. In Lithuania, the decrease in

the share of investment was mainly due to a decline in environmental

investment. Nevrtheless, the country ramped up, albeit marginally,

spending on investment in education, defence, public order and safety.

The general government debt-to-GDP ratio dropped by 3 p.p. in

the first half of 2015 to 37.6 per cent. In the second half, however, it

Expenditure on the compensation of wages to civil servants and an uptick in investment inhibited the rapid decline in general government spending triggered by high comparative base.

Chart 31. Contributions to the growth of general govern-ment spending

Investment spending as a share of the total general government expenditure has decreased in Lithuania but increased in the other two Baltic countries.

Chart 32. Change in investment as a share of general government expenditure in Poland and the Baltic countries in 2010–2013 (COFOG data)

The general government debt-to-GDP ratio decreased in the first half of 2015 but is likely to rise once again in the second half of the year due to new bond emission.

Chart 33. Contributions to the development of general government debt

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15

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2012 2013 2014 2015

Social paymentsInterestCompensation of employeesIntermediate consumptionTotal capital expenditureOther consumpion

Percentage points



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–1

0

1

2

3

Estonia Latvia Lithuania Poland

EducationHealth; social protectionHousing and community amenities; recreation, culture and religionEnvironment protectionDefence; public order and safetyGeneral public services; economic affairsTotal general government investment

Percentage points

Sources: Eurostat and Bank of Lithuania calculations.

0

5

10

15

20

25

30

35

40

45

2012 2013 2014 2015

Deposits (including saving notes)

Loans

Securities

General government debt

Percentage of GDP


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is likely to increase once again due to new bond sale. In nominal

terms, the debt decreased by more than EUR 1 billion in the first half year

of 2015, year on year, mainly due to a contraction of the long-term gov-

ernment bond portfolio following the redemption of a USD 1.5 billion bond

in January 2015. Data from the Ministry of Finance shows that the net

borrowing of the Government of the Republic of Lithuania was negative in

the first half of 2015, with a total of EUR 0.7 billion raised and EUR

1.9 billion paid back in that period. In the second half of 2015, the net

borrowing of the Government of the Republic of Lithuania is likely to be

positive once again due to two emissions of bonds, worth a combined of

EUR 1.5 billion, that were issued in October 2015 as part of preparations

to redeem a large bond emission maturing early in 2016.

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ANNEXES ANNEX 1. Factors that determine investment

Introduction

Investment is an important part of total expenditure. In Lithuania, investment comprises around 21 per cent of the coun-

try’s GDP on average. It is also an important factor of the country’s labour productivity, competitiveness and economic

growth in the long term. Therefore, it is important to find out what determines investment changes. In this Annex, the linear

regression method is used to analyse which variables have an impact on business investment in Lithuania and what share

of investment fluctuations may be explained by the variables applied. Before that the annex discusses the most popular

theoretical models of investment (accelerator, neoclassical, cash flow and Tobin’s Q model), which are used as a founda-

tion for applying the linear regression to research the factors explaining investment fluctuations. The Annex also reviews

scientific articles, which mention variables explaining investment changes that are not related to the main investment

theories.

Theories explaining investment changes

The accelerator theory model (Chenery 1952; Knox 1952) relates investment growth to the rise in production demand,

i.e. when production demand grows, enterprises invest in order to increase production volumes. This model is simple and

intuitive, however, it has shortcomings. Richard S. Brauman and Richard W. Kopcke (2001), who investigated various

investment models, stated that the simple structure of the accelerator model works best when production changes smooth-

ly and does not fluctuate much, however, the model’s accuracy deteriorates in the presence of strong economic fluctua-

tions. The view that investment mostly depends on demand factors is not contradicted by the private investment analysis

performed by the IMF, which shows that the main reason for poor private investment in developed countries in recent years

is low economic activity (IMF 2015). The IMF mentions public investment in important infrastructure objects, which increas-

es demand in the short term, as one of the factors that may encourage private investment.

The model based on neoclassical theory (Jorgenson 1963, 1971) supplements the accelerator theory with the price of

investment goods, interest rates and taxes. The model is based on the assumption that with the rise in production demand

enterprises increase production supply in order to increase profit, taking into account other production factors. According to

Richard S. Brauman and Richard W. Kopcke (2001), the neoclassical model is attractive, because it attempts to establish

the optimum amount of capital on the basis of criteria that are important in the investment environment — return on equity

and the cost of capital.

According to Tobin’s Q theory (Tobin 1969), an enterprise’s investment dynamics is explained by the ratio of the market

value of the enterprise (which is most often calculated on the basis of stock exchange data, if the firm is quoted on the

stock exchange) and the enterprise’s balance sheet value. If the enterprise’s value in financial markets is higher than the

enterprise’s balance sheet value, it may increase its value by acquiring more assets. If the enterprise’s value in financial

markets is lower than its balance sheet value, then the enterprise should invest less. Tobin’s Q theory relates financial

markets to the real sector: the rising stock value of enterprises encourages them to invest. Robert J. Barro (1990) suggest-

ed using stock price as an independent variable instead of Q. He states that stock price better reflects the expectations

regarding future profitability of the enterprise’s investment projects than the variable proposed by Tobin’s Q theory. Still,

informativeness of variables related to financial markets in explaining investment fluctuations in Lithuania is doubtful — the

country’s stock market is quite small and reflects the situation in a small part of enterprises only.

Contrary to the models discussed above, the cash flow model was created in order to evaluate the importance of vari-

ous investment financing sources: cash flows, new loans and newly issued equity capital. John R. Meyer and Edwin Kuh

(1957) paid attention to the fact that the enterprises that have large internal financial resources are more inclined to invest.

Still, after Franco Modigliani and Merton H. Miller (1958) published the theory that the enterprise’s financing structure does

not determine its value when efficient capital markets are present, the research of the interaction between profit and

investment was abandoned. According to F. Modigliani and M. H. Miller (1958), in efficient markets, enterprises can easily

borrow, issue equity securities and finance their activities from their own internal resources (allocate profits to owners in the

form of dividends or reinvest), whereas the manner of borrowing should not affect their value.

When the academic literature started to discuss market imperfections, attention wasonce again turned towards the im-

portance of profit when taking investment decisions. Steven M. Fazzari et al. (1987) provided evidence that there are

financial market imperfections, in the presence of which it is easier for a part of enterprises to finance their activities by

using internal resources than to search for external resources, i.e. to borrow or to issue new stocks. This determines the

fact that their investment decisions are limited by available internal resources. Economists analysing investment dynamics

also indicate other factors that may explain investment growth significantly. For example, large financial leverage and the

related financial risk and financial restrictions may subdue the inclination of enterprises to invest (Vermeulen 2002; Jaeger

2003; Diron and other 2005), whereas the link between investment and financial leverage may become stronger during

financial tension periods. According to Philip Vermeulen (2002), the balance sheet quality of enterprises when taking

investment decisions becomes more important during periods of recession. Marie Diron et al. (2005) stated that the link

between business investment and balance sheet indicators of enterprises is significant only in certain periods, which are

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related to recession, or when relative debt indicators increase substantially.

Among other factors that may affect investment, political uncertainty and uncertainty about the economic growth

should be mentioned — they are associated by enterprises with risk and deter them from investment. Alan Carruth

(2000) reviewed the research studies evaluating investment uncertainty and concluded that in most cases its negative

effect on investment is statistically significant. Nicholas Bloom (2009) used the model that allows researching the effect

of uncertainty and proved that even small uncertainty shocks may force enterprises to temporarily suspend hiring of new

employees and new investment.

Engelbert Stockhammer and Lucas Grafl (2010) researched business investment changes and financial uncertainty

(changes of stock indices, exchange rates and gold prices) and concluded that it significantly affects investment in the

countries, where financial markets are more integrated. Ryan Banerjee et al. (2015) researched poor investment growth

in major advanced economies in recent years and concluded that profit expectations are an important factor defining

investment. According to these researchers, higher uncertainty about the future reduces profit expectations; therefore,

enterprises invest less, whereas poor investment growth in Europe in recent years could be a consequence of economic

uncertainty. The above-mentioned IMF article indicates uncertainty of political decisions, restrictions of political decisions

and financial restrictions of enterprises, especially those that are dependent on external financing, as factors that may

subdue investment.

Research of factors determining investment fluctuations in Lithuania

When researching what determines investment in Lithuania, the data for the period from the first quarter of 1997 to

the second quarter of 2014 are used. Investment is modelled by applying linear regression. Models include differentials

of natural logarithms. All models comply with the assumptions raised for linear regressions: normal distribution of in-

vestment data10

, compliance of residuals of regressions with homoscedasticity requirements11

and stationary data

series.12

Seasonally and working day adjusted data at constant prices are used for the analysis.

Investment is modelled as gross fixed capital formation in the sectors of non-residential buildings and infrastructure

as well as transport equipment and other machinery and equipment. It is expected that these data series are related to

investment by business enterprises to the largest extent. Lagged GDP data (which are likely to reflect production de-

mand), investment (which are likely to reflect the changes in capital resources), interest rates (which are likely to reflect

capital costs), the share of profitable enterprises and overall profitability of enterprises (which are likely to reflect cash

flows of enterprises) are used as independent variables. The data used are indicated in Table A.

Table A. Data used in the analysis

Variable Explanation

Investment Gross fixed capital formation, excluding residential buildings and cultivated resources (includes other buildings and infrastruc-ture, transport equipment, other machinery and equipment), at constant prices, seasonally and working day adjusted

GDP GDP at constant prices, seasonally and working day adjusted

Interest 3-month interbank interest rate VILIBOR, CPI-deflated*

Share of profitable enterprises

Share of profitable enterprises, compared to the total number of enterprises, seasonally adjusted**

Profitability Overall profitability of enterprises (gross profit to income ratio), seasonally adjusted**

CPI Consumer Price Index

Source: compiled by authors.

* Real interest rate = (1 + 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑟𝑎𝑡𝑒𝑠)

(1 + 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑣𝑒𝑟 𝑡ℎ𝑒 𝑙𝑎𝑠𝑡 12 𝑚𝑜𝑛𝑡ℎ𝑠)− 1;

** Adjusted by applying Census X13 procedure.

In order to establish factors determining investment, six models were selected. Model 1 was created on the basis of

the accelerator theory, according to which investment depends on production demand and available capital resources.

Models 2 and 3 are based on the neoclassical theory, according to which investment is affected by capital costs, where-

as Models 46 were created taking into account the variables that reflect cash flows. The results of the application of

regressions (see Table B) do not contradict either the accelerator theory or the cash flow theory; however, they are not

favourable to the neoclassical theory. Variables that describe production demand growth (ΔBVP) are statistically signifi-

cant and positive. Accordingly, a part of variables describing cash flows (share of profitable enterprises and profitability)

are statistically significant and positive. Interest rates are among the statistically significant variables that explain invest-

ment; however, statistical significance of this variable varies and decreases when other variables are introduced in the

models. Lagged investment is not a statistically significant variable in most regression equations.

According to model 1, the GDP growth is, with a statistically significant positive link, related to investment of the

nearest four quarters. 1 p.p. faster GDP growth is linked to 1.24 per cent higher investment in the nearest quarter. In the

_________________________________ 10

Shapiro-Wilk test was used. 11

Breusch-Pagan and non-constant variance score tests were used. 12

Extended Dickey and Fuller as well as Phillips-Perron criteria were used.

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long term, 1 p.p. GDP growth change is linked to 9.45 per cent higher investment at a 1 per cent significance level. The

accelerator based model explains only slightly more than one third of investment fluctuations. The adjusted coefficient of

determination amounts to 0.35.

Table B. Results of the application of regressions

Variable Investment

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient

ΔBVP t1 1.24***

1.16***

(0.00)

(0.01)

ΔBVP t2 1.70***

1.52***

(0.00)

(0.01)

ΔBVP t3 1.86***

1.55***

(0.00)

(0.01)

ΔBVP t4 1.82***

1.47***

(0.00)

(0.00)

Interest t1

–0.13*** –0.07

–0.05

(0.01) (0.17)

(0.28)

Interest t2

–0.08* –0.05

–0.04

(0.09) (0.27)

(0.32)

Interest t3

–0.01 –0.02

0.01

(0.77) (0.71)

(0.75)

Interest t4

0.01 0.02

0.08*

(0.81) (0.69)

(0.09)

Profitable enterprises t1

0.92*** 0.85** 0.94**

(0.01) (0.02) (0.02)


0.72** 0.75** 0.59*

(0.02) (0.02) (0.09)


0.57* 0.53 0.54

(0.07) (0.11) (0.12)


0.11 0.27 0.30

(0.76) (0.47) (0.44)

Profitability t1

0.07 0.13

(0.77) (0.60)

Profitability t2

0.36 0.38

(0.16) (0.13)

Profitability t3

0.25 0.19

(0.29) (0.42)

Profitability t4

0.44* 0.40*

(0.07) (0.08)

Investment t1 0.17 0.19 0.10 –0.04 –0.10 –0.12

(0.16) (0.17) (0.44) (0.76) (0.51) (0.39)

Investment t2 –0.04 0.06 –0.01 –0.13 –0.14 -0.08

(0.76) (0.68) (0.91) (0.34) (0.31) (0.56)

Investment t3 0.04 0.15 0.09 –0.01 –0.03 0.03

(0.72) (0.28) (0.47) (0.97) (0.84) (0.79)

Investment t4 0.13 0.14 0.20 0.19 0.16 0.23*

(0.28) (0.30) (0.12) (0.14) (0.21) (0.10)

Constant 0.01 –0.01 0.00 0.00 0.01 0.00

(0.40) (0.51) (0.99) (0.63) (0.59) (0.78)

Number of observations 62 62 62 62 62 62

Adjusted R2 0.35 0.20 0.34 0.38 0.41 0.44

Source: Bank of Lithuania calculations. Note: p — value in brackets; t — the number of laggs in respective quarter. *p < 0,1; **p < 0,05; ***p < 0,01;

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Model 2, based on neoclassical theory, explains only one fifth of investment fluctuations. The adjusted coefficient of

determination amounts to 0.20. According to this model, interest rates are, with a statistically significant (1% significance

level) negative link, related to higher investment in the nearest quarter and with a statistically significant (10% signifi-

cance level) negative link to investment after two quarters. 1 per cent higher interest rates are linked to 0.13 per cent

lower investment in the nearest quarter. In the long term, the respective increase is linked to 0.48 per cent lower invest-

ment at a 10 per cent significance level.

Model 4, based on cash flows, explains investment better than the above-mentioned models; however, more than

half of investment fluctuations still remain unexplained. The adjusted coefficient of determination amounts to 0.38.

According to model 4, the number of profitable enterprises, compared to the total number of enterprises is statistically

significantly (5% significance level) positively linked to investment in the nearest two quarters and statistically significant-

ly (10% significance level) positively linked to investment after three quarters. 1 per cent higher share of profitable

enterprises is linked to 0.92 per cent higher investment in the nearest quarter and 2.35 per cent higher investment in the

long term at a 5 per cent significance level.

Conclusions

Summarising the results obtained, we cannot reject the assumption that demand factors, which are underlined by the

accelerator and cash flow models, have an effect on investment in Lithuania, i.e. economic activity and cash flows of

enterprises affect investment by enterprises. We cannot reject the possibility that investment is affected by capital costs

as well, i.e. enterprises invest relatively more when interest rates are lower. However, the model based on capital costs

gave the poorest explanation of investment fluctuations, whereas interest rates were not a statistically significant variable

in other models. The analysis also did not show a link between investment in the current period and in the nearest

period. Moreover, the selected models fail to explain a large part of investment fluctuations, most likely because the

models do not include certain additional factors that may explain investment, for example, variables reflecting financial

restrictions of enterprises or uncertainty of the economic environment.

References

Banerjee, R., Kearns, J. & Lombardi, M. J. (2015). “(Why) is Investment Weak?”, BIS Quarterly Review, March 2015.

Barro, R. J. (1990). “The Stock Market and Investment”, Review of Financial Studies 3(1), p. 115–131.

Bloom, N. (2009). “The Impact of Uncertainty Shocks”, Econometrica 77(3), p. 623–685.

Brauman, R. S. & Kopcke R. W. (2001). “The Performance of Traditional Macroeconomic Models of Businesses’ Invest-

ment Spending”, New England Economic Review 2, p. 3–39.

Carruth, A., Dickerson, A., & Henley, A. (2000). “What Do We Know About Investment Under Uncertainty?”, Journal of

Economic Surveys 14(2), p. 119.

Chenery, H. B. (1952). “Overcapacity and the Acceleration Principle”, Econometrica 20(1), p. 1–28.

Diron, M., Manzano, M. C. & Westermann, T. (2005). “Forecasting Aggregate Investment in the Euro Area: Do Indicators

of Financial Conditions Help?”, Bank for International Settlements, Working Paper.

Fazzari, S., Hubbard, R. G. & Petersen, B. C. (1987). “Financing Constraints and Corporate Investment”, National

Bureau of Economic Research, Working Paper.

IMF 2015. “Private Investment: What’s the Holdup? World Economic Outlook: Uneven Growth — Short- and Long-Term

Factors”, April, p. 111–143.

Jaeger, M. A. (2003). “Corporate Balance Sheet Restructuring and Investment in the Euro Area”, International Monetary

Fund, Working Paper.

Jorgenson, D. W. (1963). “Capital Theory and Investment Behavior”, The American Economic Review 53(2), p. 247–

259.

Jorgenson, D. W. (1971). “Econometric Studies of Investment Behavior: A Survey”, Journal of Economic Literature 9(4),

p. 1111–1147.

Knox, A. (1952). “The Acceleration Principle and the Theory of Investment: A Survey,” Economica 19, p. 269–297.

Meyer, J. R. & Kuh, E. (1957). “The Investment Decision: An Empirical Study”, Reviewed Work.

Modigliani, F. & Miller, M. H. (1958). “The Cost of Capital, Corporation Finance and the Theory of Investment”, The

American Economic Review 48, p. 261–297.

Stockhammer, E. & Grafl, L. (2010). “Financial Uncertainty and Business Investment”, Review of Political Economy

22(4), p. 551–568.

Tobin, J. (1969). “A General Equilibrium Approach to Monetary Theory”, Journal of Money, Credit and Banking 1(1), p.

15–29.

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Vermeulen, P. (2002). “Business Fixed Investment: Evidence of a Financial Accelerator in Europe”, Oxford Bulletin of

Economics and Statistics 64(3), p. 213–231.

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0

400

800

1200

1600

2000

LU BE

NL IR FR

UK SI

ES

MT

GR PT

PL

HR

EE SL

HU LV CZ LT RO

BG

Source: Eurostat.

EUR

0

10

20

30

40

50

SI

FR

*

LU LT MT

HU PL

NL

PT

LV IR UK

BG SL

HR

RO EE

ES

CZ

* 2012. Source: Eurostat.

Per cent

ANNEX 2. The effect of minimum wage increase on macroeconomic variables: survey re-sults of Lithuanian enterprises

Introduction

In 2006, the European System of Central Banks (ESCB) launched a Wage Dynamics Network (WDN) with the aim to

deeper understand features and sources of wage and labour costs dynamics in the EU Member States. Based on micro-

data evidence collected by the survey from the EU enterprises, it was intended to better understand cost dynamics, to

relate them to price setting behaviour and thereby give signals for monetary policy decision makers. The first wave of the

WDN survey (WDN1) was conducted in 2007, the second one (WDN2) — in 2009, and the third wave of the WDN

survey (WDN3) was launched in 2013 with the non-core and core reference periods of 2008–2009 and 2010–2013

respectively. Lithuania was involved in the first and in the third waves of the WDN project. The Lithuanian version of the

WDN3 survey consisted of four core and two non-core blocks of questions, in particular, the core blocks on Information

about the firm, Changes in the economic environment, Labour force adjustments and Wage adjustments and non-core

blocks on Price setting and price changes and Minimum wage increase.

This annex presents a short description of the results of the non-core block on Minimum wage increase. Lithuania

experienced a substantial increase in minimum wage in January 2013 (by approximately 17%: from EUR 246 to EUR

290 or, equivalently, from LTL 850 to LTL 1,000); therefore, information extracted from the survey is particularly im-

portant to understand how Lithuanian enterprises managed to adjust to the increase in labour costs.

Institutional background — minimum wages in Lithuania

Lithuania belongs to the group of countries having statutory minimum wage. Minimum wages in Lithuania are set uni-

formly on the highest — national — level of economy and in the presence of low union and collective wage system

coverage13

, minimum wage could be considered as an important indicator for characterising institutional wage determi-

nation structure in the country. Minimum wage coverage14

in October 2013 was 10.5 per cent (see Chart A) after its

substantial increase by approximately 17 per cent (from EUR

246 euro to EUR 290 or from LTL 850 to LTL 1,000) in Janu-

ary 2013.

The level of minimum wages differs substantially across

countries, and Lithuania has one of the lowest minimum wage

levels among the EU countries (see Chart B). However, this

indicator is usually not considered to be an indicator character-

ising labour market institutional system. Instead, minimum

wage to average (or median) wage ratio is considered to

represent labour market institutions; this measure could also

be used for comparability reasons among countries. Minimum

wage to average wage ratio in Lithuania is among the highest

ones in the EU — 47.3 per cent in 2013 (see Chart C), though

with considerable divergences across the NACE 2 sectors in

economy. In 2013, the lowest minimum to average wage ratio

was in financial intermediation (19.4%), whereas the highest

— in accommodation and food service activities (74.3%).

Chart B. Minimum monthly wage in the EU in the first quarter of 2014 Chart C. Minimum wage to average wage ratio in the business eco-nomic activities in the EU in 2013

_________________________________ 13

According to Statistics Lithuania, union density was around 7 per cent in Lithuania in 2013. This measure is calculated as a ratio of total union members to the total number of employed in the economy. Collective bargaining coverage is likely to be somewhat higher: EC estimates show that around 15 per cent of all employees in the economy were covered by collective agreements in 2009 (see http://www.worker-participation.eu/National-Industrial-Relations/Countries/Lithuania/Collective-Bargaining). 14

Minimum wage coverage is an indicator denoting percentage of employees in the economy receiving minimum wages.

Chart A. Distribution of employees according to wage level in Lithuania in 2013–2014

0

2

4

6

8

10

12

MW

*

MW

*–35

0 E

UR

351–

400

EU

R

401–

450

EU

R

451–

500

EU

R

501–

600

EU

R

601–

700

EU

R

701–

800

EU

R

801–

900

EU

R

901–

1000

EU

R

1001

–130

0 E

UR

1301

–160

0 E

UR

1601

–200

0 E

UR

2001

–300

0 E

UR

3001

EU

R a

nd m

ore

2013 2014

* In Jan 2013 – EUR 290 , in Oct 2014 – EUR 300

Per cent


http://www.worker-participation.eu/National-Industrial-Relations/Countries/Lithuania/Collective-Bargaining

http://www.worker-participation.eu/National-Industrial-Relations/Countries/Lithuania/Collective-Bargaining

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WDN3 survey results: minimum wage coverage in Lithuania

The WDN3 survey results indicate slightly higher minimum wage coverage in Lithuania as compared to figures calcu-

lated on the basis of Statistics Lithuania data (10.5% in October 2013). Results are presented in Table A, indicating that

after the minimum wage increase in January 2013 its coverage in the economy increased from 15.3 to 17.8 per cent.15

Table A. Minimum wage coverage in Lithuania (per cent)

Enterprises according to their size Enterprises according to their activity

Economy (total)

Small Medium Large Very large Manufacturing Construction Trade Business services

Financial intermediation

Before minimum wage increase in January 2013

15.3 29.7 17.9 11.9 5.0 16.2 15.3 16.6 14.2 4.5

After minimum wage increase in January 2013

17.8 35.6 18.3 15.0 5.4 20.4 17.2 16.7 17.5 5.9

Notes: employment-weighted averages. Sources: Lithuanian WDN3 survey and Bank of Lithuania calculations.

The WDN3 survey results also show that minimum wage coverage tends to correlate with the size of enterprises —

higher coverage is found among small enterprises (29.7–35.6%) and lower — among large and very large enterprises

(11.9–15.0% and 5.0–5.4% respectively).16

Labour intense production sectors — construction, trade, business services

and especially financial intermediation — typically report somewhat lower minimum wage coverage as compared to manu-

facturing. This tendency could be directly linked to compositional effects of the labour force — the manufacturing is charac-

terised by the highest share of lower skilled (both manual and non-manual) employees in its labour force.17

WDN3 survey results: adjustment channels to minimum wage increase

Enterprise-level results for Lithuania reveal that the increase in minimum wages caused the increase in total labour

costs by approximately 11.1 per cent. Table B lists these results. The impact tended to be higher for those enterprises that

reported higher shares of minimum wage receivers in their labour force — small enterprises experienced higher labour cost

increase (12.7%) than the economy on average. By sectors, higher cost increase was reported by manufacturing (14.5%)

and construction (18.0%). Rise in labour costs was naturally followed by some adjustment strategies, i.e. reduction in

employment, increase in prices and the overall efficiency as well as reduction in non-labour costs. Such adjustment chan-

nels were selected in accordance with the theoretical models.18

Table C summarises the enterprise-level results.

Table B. Increase in total labour costs following minimum wage increase in January 2013 (per cent)


Economy (total) Small Medium Large Very large Manufacturing Construction Trade Business services Financial intermediation

11.1 12.7 7.0 5.0 3.6 14.5 18.0 7.9 11.1 4.2

Notes: enterprise-weighted averages. Sources: Lithuanian WDN3 survey and Bank of Lithuania calculations.

Table C. Strategies of enterprises used to deal with increase in minimum wages

(percentage of enterprises for which these strategies were relevant or very relevant)


Economy (total)

Small Medium Large Very large Manufacturing Construction Trade Business services

Financial intermediation

Lay off people 1.6 2.0 0.0 1.2 0.0 0.5 4.8 0.0 2.6 0.0 Hire less 8.7 10.7 2.3 2.3 4.5 7.2 13.9 6.3 9.8 3.7 Increase product prices 8.3 9.5 5.5 1.5 8.9 13.4 10.0 4.8 8.8 2.0 Reduce non-labour costs 18.1 20.6 9.7 11.1 13.4 29.0 17.9 12.9 18.4 13.3 Increase productivity 24.6 25.4 23.3 18.8 23.0 43.2 32.0 18.2 19.9 16.5 Other 63.4 66.6 57.8 0.0 0.0 55.7 100.0 52.4 66.1 33.3

Notes: enterprise-weighted averages. Sources: Lithuanian WDN3 survey and Bank of Lithuania calculations.

According to the survey results, the effect of minimum wage increase on product prices is marginal — 8.3 per cent of

Lithuanian enterprises reported this measure to be relevant or very relevant. The effect of minimum wage increase on

employment due to layoffs is small. Only a minor fraction of Lithuanian enterprises (less than 2%) reported that they used

the strategy of reducing the number of employees (i.e. this strategy was reported to be relevant or very relevant for the

enterprises). Decisions to hire less, which affect employment due to lower new job creation, were taken by a higher share

of enterprises (almost 9%), but the most popular adjustment strategies were, however, not related to changes in prices

_________________________________ 15

It is important to note that minimum wage coverage, calculated on the basis of the Statistics Lithuania data, covers all sectors in economy. The enterprises covered by the survey were operating in manufacturing (C), construction (F), trade (G), business services (H, I, J, L, M, N) and financial intermediation (K) sectors in accordance to NACE 2 whereas other sectors were excluded. Based on Statistics Lithuania data, minimum wage coverage in these sectors was 12.1 per cent in 2013. 16

Small enterprises are defined as enterprises having from 5 to19 employees, medium size enterprises — from 20 to 49 employees, large enterprises — from 50 to 199 employees, and very large enterprises — over 200 employees. 17

This information is also retrieved from the WDN3 survey. 18

Literature on labour market issues states that legislation of minimum wage falls under the definition of labour market institutions that cause wage rigidity and possibly has adverse effects on employment. Empirical literature on the topic usually finds no or little effect of employment response. It is evident that several other adjustment channels are applied that explain small or negligible impact of minimum wages on unemployment. The most common channels are reduction in hours worked, reduction in non-wage benefits, reduction in training, changes in employment composition, higher prices, improvement in efficiency, reduction in profits and some others. In regard to empirical evidence on the adjustment channels, the impact of minimum wages on price increase or reduction in profits is found to be usually small, but present; the effect on other variables is usually indefinite (see Nickell 1997; Nickell et al. 2002; Nickell et al. 2005; Bassanini and Duval 2006; Schmitt 2013).

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or employment. Reduction in non-labour costs and increase in productivity were more extensively used as measures of

reaction to the increase in labour cost (18.1% and 24.6% respectively). Among other adjustment channels (used by

more than 60% of Lithuanian enterprises), reduction in bonuses, working time adjustments, increase in efficiency (new

technologies, increase in overall efficiency) and even efforts to increase revenue were used as reactive policies by the

enterprises. Increase in minimum wage also brought about a need to raise wages to the employees earning more than

minimum wages; this effect was reported by more than one fourth (25.4%) of all Lithuanian enterprises (i.e. by those

having and not having minimum wage receivers). These tendencies could reflect a substantial proportion of employees

receiving wages just slightly above the minimum wage level in the economy; however, an assumption that the wage of

higher income earners was not changed when the minimum wages was increased cannot be ignored, although wage

adjustment for higher income earners, associated with the increase in minimum wages, should not be ignored as well.

Differences across the enterprise size bins are present and highly related to the minimum wage coverage as well as

magnitude of the increase in total labour costs. Small enterprises reported more often that they applied adjustment

measures, though there are no obvious signs that some particular measures were preferred more to others by enterpris-

es across the different enterprise size bins. Distribution of measures across sectors tends to differ more. For example,

the manufacturing sector reported more frequently the increase in productivity, whereas the construction sector regularly

applied other policies to offset the increase in labour costs. Also, enterprises operating in construction and business

services, compared to the average of the total economy, more often reported decisions to reduce the number of employ-

ees and to hire less new ones.

Minimum wage coverage in the enterprises appeared to be an important indicator that determines cost adjustment

strategies. The importance of this indicator is also proven when linking the differences of strategy implementation with

the minimum wage coverage intervals in Lithuanian enterprises.19

A strategy, usually implemented by enterprises having

up to 25 per cent of minimum wage receivers in their labour force was to target other costs and employ other cost-

adjustment policies. Those enterprises having a high coverage — above 25 per cent — of minimum wage receivers

made somewhat different adjustment decisions, although other policies remained an important element in dealing with

the labour costs increase. Decisions to hire less and increase the product prices are more relevant when the minimum

wage coverage is high.

Conclusions

To conclude, the summarised WDN3 results showed that due to the increase in minimum wage in January 2013,

8 per cent of Lithuanian enterprises had to increase prices. Cut in the number of employees was reported by approxi-

mately 2 per cent, whereas decisions to hire less were taken by approximately 9 per cent of the enterprises. The remain-

ing enterprises — the vast majority of them — applied other minimum wage channels of adjustment, which were not

related to prices or employment.

Compared to the whole economy, small enterprises reported a higher increase in labour costs due to the minimum

wage increase in Lithuania — this relates to higher minimum wage coverage in these enterprises. Increase in minimum

wage also brought about a need to raise wages to those employees that earned more than minimum wages; this effect

was reported by more than one fourth of all Lithuanian enterprises.

Minimum wage increase makes an effect on employment only, if the minimum wage coverage is high; otherwise,

Lithuanian enterprises prefer other adjustment channels. Decisions to hire less, related to the new job creation, become

much more relevant with the increase in minimum wage coverage. The same reasoning applies for decisions to increase

product prices. In the presence of low minimum wage coverage at Lithuanian enterprises, other policies are often

applied when dealing with the cost increase.

It should be also noted that the results reported in this annex only examine the effect of a specific minimum wage

increase, i.e. that of January 2013. In Lithuania, the year 2013 brought a steady economic growth, increasing corpora-

tion profitability indicators and an emerging problem of skill-mismatch (i.e. insufficient labour supply of required skills) in

the labour market. Under worse overall economic conditions, responses of Lithuanian enterprises to the equivalent

increase in minimum wage might be different. Minimum wage to average wage ratio is also one of the highest in Lithua-

nia compared to the other EU counties. Further increase in minimum wage, therefore, might lead to different minimum

wage channels of adjustment than reported in this annex analysing the effect of the minimum wage increase in January

2013.

References

Bassanini, A., Romain, D. (2006). “Employment Patterns in OECD Countries: Reassessing the Role of Policies and

Institutions”, OECD Economic Department Working Paper No. 486.

Nickell, S. (1997). “Unemployment and Labour Market Rigidities: Europe versus North America”, Journal of Economic

Perspectives, Vol. 11, No 3, p. 55–74.

_________________________________ 19

Intervals: 0–25 per cent, 26–50 per cent, 51–75 per cent and 76–100 per cent of minimum wage receivers.

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Nickell, S., Nunziata, L., Ochel, W., Quintini, G. (2002). “The Beveridge Curve, Unemployment and Wages in the OECD

from the 1960s to the 1990s”, Centre for Economic Performance, London School of Economics and Political Science,

London, UK.

Nickell, S., Nunziata, L., Ochel, W. (2005). ”Unemployment in the OECD since the 1960s. What Do We Know?”, The

Economic Journal 115 (January), p. 1–27.

Schmitt, J. (2013). “Why Does the Minimum Wage Have No Discernible Effect on Employment?”,Center for Economic and

Policy Research, February 2013.

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50

60

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1945 1955 1965 1975 1985 1995 2005

Thousands


65

70

75

80

85

90

95

100

15 20 25 30 35 40 45 50 55 60

Per cent


Age in 2015

–1.0

–0.5

0.0

0.5

1.0

1.5

2.0

2.5

2001 2005 2009 2013 2017 2021 2025 2029

Entering working age group

Leaving working age group

Difference between entrants and leavers

Per cent, compared to the working age population


Note: Shaded area indicates forecast based on migration and mortality assumptions.

ANNEX 3. Working age population and labour force development and outlook

Introduction

In the long run, economic growth potential depends on technological

progress, the amount of capital and labour force development. The latter

factor depends on the working age population and the share of the

population deciding to participate in the labour market (labour force

participation rate).20

The age composition of the working age population is

also important: both the youngest and the oldest representatives of the

working age population participate in the labour market less frequently;

therefore, when these people comprise a large share of the population,

the labour force is smaller.

This Annex discusses the factors that determined the working age

population development and draws the death rate and net migration

assumptions of the population aged up to 80 years. These assumptions

are used to make the population forecast, covering the period up to 2030.

These forecast and labour force participation rate assumptions are used

to create three labour force development scenarios, covering the labour

force aged 15–79 years and the period up to 2030.

Working age population development and outlook

The working age21

population in Lithuania was 2.315 million in 2001. Over

14 years, the population decreased by 15.8 per cent (367 thousand) and

comprised 1.949 million in 2015. The rate of decline fluctuated noticeably:

the slowest decline was observed in 2003 (0.21%), whereas the fastest

decline was recorded in 2010 (3.49%). The average annual decline of the

population made up 1.22 per cent, or 26.2 thousand.

Each year, the working age population is supplemented by people that

reach the age of 15 and left by people that reach the age of 65. In the

period from 2001 to 2014, the number of people entering this population

group was higher than the number of people leaving it; however, this

difference was constantly narrowing: the working age population in-

creased by 0.9 per cent in 2001 and by just 0.1 per cent in 2014 (see

Chart A). The decline in this difference is determined by birth rate chang-

es that occurred in the last decade of the 20th

century. Until 1993, the

number of newborns in Lithuania was from 51.8 thousand to 63.0 thou-

sand per year, but later this number declined by almost a half: in the

period from 2002 to 2014, the number of newborns was around 30.3

thousand per year (see Chart B). Emigration also contributed to the lower

number of people supplementing the working age population. Since 2001,

the emigration of people aged 0 to 15 years has been higher each year

than their immigration, which means that a certain share of representa-

tives of this age group emigrates before reaching working age. If the

population have not been emigrating since 2001 and the death rate would

have been usual, in 2015, the number of people aged 15 would be 9.5 per

cent higher than it actually is (see Chart C).

Working age population is also reduced by deaths. In the period from

2001 to 2014, the working age population declined due to deaths by

0.51–0.68 per cent each year. Even though the death rate of the working

age population has been declining constantly in Lithuania, it is still the

highest in the EU (see Chart D) and this is not related to the age of this

population group, i.e. to the fact that a relatively large part of it consists of

older people. The death rate of almost every age group covering one year

(17 years, 18 years, etc.) is the highest in the EU. On average, it is 2.9

times higher than in the old EU countries and 1.7 times higher than in the

new EU countries. The poorest death rate situation is among people

________________________________ 20

This Annex analyses only the quantitative aspect of the labour force, i.e. its size. The human capital, which is the factor that shows labour force quality, is

not analysed 21

Working age is the age interval of 15 to 64 years, which is usually applied in the labour market analysis. The share of people older than 64 years, participat-

ing in the labour market, is significantly lower; therefore, the effect of changes in the size of this population on the labour force is not very significant.

Chart A. Number of persons entering and leaving the working age population group

Chart B. Number of newborns

Chart C.Actual population in 2015, compared to the potential population, which would have been reached, if people were not emigrating since 2000

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15

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

15 20 25 30 35 40 45 50 55 60

Old EU Member States

New EU Member States

Lithuania

Per cent


Age

–1.2

–0.8

–0.4

0

0.4

0.8

1.2

1.6

2.0

0 5 10 15 20 25 30 35 40 45 50 55 60

Net migration

Emigration

Immigration

Per cent


Age

–4.0

–3.5

–3.0

–2.5

–2.0

–1.5

–1.0

–0.5

0.0

0.5

1.0

–4.0

–3.5

–3.0

–2.5

–2.0

–1.5

–1.0

–0.5

0.0

0.5

1.0

2001 2005 2009 2013 2017 2021 2025 2029

Net migration

Deaths

Difference between entrants and leavers of the working age group

Population (rh scale)

Sources: Eurostat, Statistics Lithuania and Bank of Lithuania calculations.


slightly younger than the middle age (30–44 years), i.e. their death

rate is around 3.6 times higher than that of the same age group in

the old EU countries. Such differences affect the working age

population in Lithuania significantly: if the death rate of people in

each age group covering one year would have been the same as in

the old EU countries, deaths would have reduced the working age

population by 0.3 per cent instead of 0.6 per cent annually in the

period from 2006 to 2012.

Working age population is also affected by migration. In the pe-

riod from 2001 to 2014, the number of people who emigrated was

higher than that of people who immigrated, i.e. net emigration was

present, and this phenomenon reduced the working age population

by 1.2 per cent on average each year. Nevertheless, the effect of

net emigration fluctuated significantly. In 2005, at the beginning of

Lithuania’s membership in the EU, net emigration increased sub-

stantially and reduced the working age population by 1.9 per cent,

whereas the effect of net emigration was even stronger at the end of

the economic crisis, namely in 2010, when it comprised 3.3 per

cent.22

However, in 2008, when the unemployment rate was very

low, the effect of net emigration was weaker (0.6%). Thus, migration

has the strongest effect among all the factors affecting population.

Net emigration is the highest in the age group of young persons.

As shown in Chart E, net emigration of the population aged 18 to 23

is higher than that of population younger than 18 years. 18 to 23 is

the age when people graduate from school or studies and start

searching for a job. Net emigration of people older than 23 years

declines as the age increases. This means that migration affects not

only the working age population, but also the composition of this

group. The migration since 2001 has made the largest impact on the

decline in the number of younger population. For example, owing to

migration, the population aged 25–34 years is lower by 28.4 per

cent in 2015 than the potential number that would have been

reached, if people have not been emigrating since 2000 and the

death rate would have been usual (see Chart C).

Assumptions concerning future migration and the death rate are

required to make the working age population forecast. It is not

necessary to make assumptions concerning the number of people

supplementing and leaving the future working age group, since it will

be known after making the above-mentioned assumptions. For

example, people born in 2014 will supplement the working age

population group in 2029; therefore, after adjusting the number of

these people by their expected death rate and migration, we will

know how many 15-year-old persons will live in Lithuania in 2029.

In contrast with the current situation, the number of persons

leaving the working age population group will be higher than the

number of persons supplementing this group in the future (see

Charts A and F). In the coming years the number of the latter will

decline; however, in 2018 this number should stabilise (the number

of newborns has not been declining since 2003). Around the same

time, i.e. in 2019, the number of people leaving the group will start to

increase. Thus, the difference of the numbers of people supplement-

ing and leaving the group will increasingly reduce the number of the

working age population: this effect will be 0.1 per cent in 2015,

whereas in 2027 it will reach as much as 0.8 per cent. It is as-

sumed that the death rate will continue to decline in the future. In

2014, the death rate of all age groups covering one year in the

population aged 0 to 64 years was lower than in 2001 by 25.0 per

________________________________ 22

It is likely that net emigration was lower in 2010 than indicated by migration statistics. Legislative amendments determined the fact that the number of emigrants of that year included a part of people who emigrated earlier than in 201

Chart D. Death rate of the working age population in 2013

Chart E. Emigration, immigration and net migration levels in 2013

Chart F. Contributions to working age population development

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67

69

71

73

75

77

79

2001 2005 2009 2013 2017 2021 2025 2029

First assumption

Second assumption

Third assumption

Per cent


–2.5

–2.0

–1.5

–1.0

–0.5

0.0

0.5

1.0

2011 2015 2019 2023 2027

Scenario based on first participation rate assumption

Scenario based on second participation rate assumption

Scenario based on third participation rate assumption



cent on average. This trend of decline should be taken into account since the forecast period is quite long (15 years). If

an assumption is made that the death rate will decline as fast in the future as it has declined over the last 13 years, i.e.

the death rate of each one year group will decline each year by the same percentage as it had declined on average over

one year in the period from 2001 to 2014, it can be calculated that deaths will reduce the working age population by 0.51

per cent each year, whereas later this effect will decline and comprise 0.45 per cent in 2030 (see Chart F). The assump-

tion of constant death rate23

would result in a 0.08 per cent larger decline of the population on average. The death rate

assumption applied by the Eurostat when making population forecasts (EUROPOP2013) would reduce the population in

Lithuania by 0.04 p.p. less on average than the assumption made in this Annex.

Migration is affected by various economic factors; therefore, the assumption concerning future migration is related to

very high uncertainty. For example, the increase in net emigration during the economic crisis shows that it is affected by

the labour market situation in Lithuania and foreign countries. It is particularly difficult to forecast the development of

these markets in the long term. Nevertheless, one factor is less uncertain. Relatively large income gap between Lithua-

nia and Western European countries will further lead to net emigration, even though, as the income level in Lithuania

approaches that of Western European countries, net emigration should decline gradually. Therefore, an assumption is

made that the level of net emigration24

in age groups covering one year will decline and comprise 50 per cent of its

2014-level in 2029. Therefore, the effect of net emigration on the working age population will gradually decline: this

factor will reduce it by 0.46 per cent in 2015, whereas its effect in 2029 will be just –0.23 per cent (see Chart F).

The three discussed factors added together will determine a quite fast decline of the working age population. In the

period from 2015 to 2029 it will decline by 1.36 per cent on average

annually (see Chart F). This rate of decline is similar to the one observed

in the period from 2001 to 2014 (1.22%). However, in that period two

events that highly accelerated population decline occurred: Lithuania

joined the EU and in 2008-2009 the country suffered the economic

crisis. Thus, even if there are no additional factors, which could acceler-

ate the population decline in the period from 2015 to 2029, the popula-

tion should decline faster in this period than over the last 13 years.

Although net emigration and deaths will reduce the population slightly

less, the difference in the numbers of people supplementing the working

age population group and those leaving it will increase rapidly (see Chart

F). This will determine the fact that, after declining by 1.24 per cent in

2016, later the decline of the working age population will accelerate and,

for example, in 2026 it will reach as much as 1.54 per cent. In later years

the decline will decelerate, since the said difference in numbers will

gradually narrow.

Labour force development and outlook

When forecasting the labour force, it is assumed that its annual

growth will be determined by three factors: the change in the population

aged 15–64, the change of the labour force participation rate of this

population and the change of the labour force covering the age group of

65–79 years.25

The forecast is based on the already discussed popula-

tion forecast and the assumptions concerning the labour force participa-

tion rate. The decision of residents to participate in the labour market,

same as in migration, is affected by various factors; therefore, labour

force participation rate assumptions are related to a high degree of

uncertainty. Three assumptions are made in order to evaluate various

participation rate development possibilities:

1) the participation rate of all 5-year age groups will not change dur-

ing the review period and will remain the same as in 2014;

2) the participation rate of those age groups, whose participation

rate increased noticeably during the economic recovery period,

will continue to increase, but the rate of increase will be slower by

________________________________ 23

I.e. that the death rate of each age group will remain unchanged and will be equal to the average death rate of respective group in the period from 2012

to 2014. 24

Difference in the numbers of immigrants and emigrants, divided by the population. 25

When the Eurostat presents its statistics, the labour force consisting of the population older than 74 years is not divided into 5-year groups; therefore, in

this Annex that labour force is allocated to the age interval of 75 to 79 years.

Chart G. Participation rate of the population aged 15 to 64 years calculated according to the assumptions concerning activity levels of 5-year age groups

Chart H. Labour force development scenarios

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–6

–5

–4

–3

–2

–1

0

1

2

3

4

–6

–5

–4

–3

–2

–1

0

1

2

3

4

2002 2006 2010 2014 2018 2022 2026

Population aged 15–64

Participation rate of persons aged 15–64

Labour force aged 65 and older

Labour force (rh scale)



–6

–5

–4

–3

–2

–1

0

1

2

3

4

–6

–5

–4

–3

–2

–1

0

1

2

3

4

2002 2006 2010 2014 2018 2022 2026







–6

–5

–4

–3

–2

–1

0

1

2

3

4

–6

–5

–4

–3

–2

–1

0

1

2

3

4

2002 2006 2010 2014 2018 2022 2026







half26

; the participation rate of those groups, whose participation rate remained broadly unchanged during that peri-

od, will not change and will remain the same as in 2014;

3) the difference of the participation rate in Lithuania and the average participation rate in the three EU countries that

have the highest rate in each 5-year age group will decline in half by 2029.

By applying these assumptions, the participation rate of the population was calculated (see Chart G) and labour force

development scenarios were created (see Chart H). When creating these scenarios, the same population forecast is used;

therefore, the effect of the population on the labour force is the same in all cases. It reflects the extent of the labour force

increase or decrease due to population changes, if the labour force participations rate remains unchanged. These changes

increase or reduce the labour force by the same percentage as the percentage of the population change.27

Thus, the

negative effect of the population (its annual average will be 1.36%) on the labour force will be stronger in the period from

2015 to 2029 than over the last 13 years (see Charts I–K). Moreover, this effect will increase: the labour force will decline

due to the population by 1.24 per cent in 2016, whereas in 2026 this decline will reach as much as 1.54 per cent. Accord-

ing to all scenarios, the effect of the population will be stronger than that of the labour force participation rate.

The influence of the participation rate on the labour force shows the extent that the rate change increases or reduces

the labour force, when the population remains unchanged. According to the first scenario, the participation rate does not

have a direct impact on the size of the labour force, since the participation rate of all 5-year age groups remains un-

changed. However, the participation rate of the whole group, for example, covering the population aged 15–64, may

change even if rates of subgroups do not change. This happens when the composition of the working age population group

changes, for example, the share of groups with a high participation rate grows. This is shown by the participation rate of

the population group aged 15–64 calculated on the basis of the first assumption (see Chart G). The participation rate of this

group should increase in the coming 5 years due to the changing population age composition in this group — in 2020 it

should be 1.0 p.p. higher than in 2014, whereas later it should decline by a similar percentage. Such participation rate

development in the period from 2015 to 2020 will increase the labour force by 0.2 per cent on average annually (see Chart

I). This effect is not high — the rising participation rate during the economic recovery period had increased the labour force

by 1.2 per cent on average annually.

The effect of the participation rate on the growth of the labour force is more favourable according to the second and

third scenarios. According to the second scenario, the participation rate of the population aged 15–64 will increase quite

noticeably in the coming 5 years — by 2.1 p.p. (see Chart G). It will also be positively affected by the changes in the

population age composition. The labour force increases due to these factors by 0.5 per cent on average annually (see

Chart J). In later years, the changes of the population age composition will become unfavourable; the participation rate will

grow slower and will stop growing in 2025. Thus, in the period from 2020 to 2029, the labour force will grow much slower

due to participation rate changes — by 0.1 per cent on average annually. According to the third scenario, the participation

rate will rise faster in the coming 5 years and then the growth will slow down. In 2025, the the participation rate growth will

stabilise and rise by 0.2 p.p. annually. The effect of the participation rate on the labour force in the coming 5 years should

be 0.6 per cent on average annually, whereas in the period from 2020 to 2029 it will be noticeably smaller — 0.2 per cent

(see Chart K).

________________________________ 26

It is assumed that the annual increase (in percentage points) in the activity of young (aged 20–24) and older (aged 55–79) people will be lower by half than

the average increase in the period from 2011 to 2014. When calculating the average, a particularly fast activity increase in 2014 is reduced in half, since it

could be determined by one-off factors, such as reforms implemented at that time (decentralisation of social support, legalisation of receipts when providing

agricultural services, etc.). 27

If the population grew by 10 per cent and its activity level remained unchanged, the labour force would also increase by 10 per cent.

Chart I. First labour force development scenario Chart J. Second labour force development scenario

Chart K. Third labour force development scenario

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Conclusions

The analysis shows that the labour force will decline at an accelerating pace. In 2016, the labour force might contract

by up to 0.8 per cent, while in 2026 it could decline by 1.1–1.6 per cent. Accelerating decrease of the working age

population will be the main drive for the decline in the labour force. Other factors — the population age structure and

their participation rate — will have relatively minor impact. Due to changes in the population age structure, the labour

force will increase slightly over the next 5 years, but later it will decline a little. The positive impact of the rising participa-

tion rate will only partly offset the negative impact of population decline. The risk that the labour force will be different

than projected is quite high. It is mainly related to the migration and labour force participation developments.

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ANNEX 4. Analysis of profit indicators calculated from national accounts

Introduction

Inflation in Lithuania and its fluctuations are determined substantially by external factors — trends in international food

and energy commodities markets. This dependence is related to high volatility of commodity prices and a significant share

of food and energy goods in the basket of Lithuanian consumers, which is substantially larger than the euro area average.

Domestic factors also have a significant impact on inflation. Sometimes their impact is of the same direction as that of

external factors, sometimes it is opposite. For example, during the economic upturn inflation was increased both by the

prices dependent on external factors (food, fuel and administered prices grew due to a rapid increase in global commodity

prices) and by the prices that are more related to domestic factors, which cover industrial goods and market services (core

inflation, which reflects the growth of these prices, exceeded 6% in 2008). Contrary to the economic upturn period, now the

prices dependent on the external situation and those dependent on the domestic situation have an opposite effect on

inflation: prices related to commodity markets fall, whereas prices more related to the domestic situation rise consistently.

Inflationary pressure related to domestic factors can be estimated by using national accounts data. A significant indica-

tor which allows evaluating this pressure is the GDP deflator. It is the ratio of nominal GDP (value of goods produced) to

real GDP (volume of goods produced); therefore, we can say that it reflects the price of a unit of production. It can be

broken down into labour costs, profits and taxes — domestic factors that determine price changes. After the breakdown of

the GDP deflator and its changes into these components, we can see which domestic factors have a larger effect and

which have a smaller effect.

With regard to domestic pressure on prices, it is common to pay most attention to the development of unit labour costs.

They are projected according to the labour market situation and the situation of the whole economy. Less attention is paid

to profit, which is analysed and modelled as a residual value obtained by subtracting labour costs from income. However,

profit indicators are important as well. Their development reflects the capacity of enterprises to change the prices of goods

produced and services provided when costs change; therefore, they are very closely related to the market situation. For

example, if labour costs change when production demand is high, enterprises may raise production prices and thus main-

tain the profit earned or even earn higher profit. If labour costs increase when the economy is in downturn (for example,

due to the minimum wage increase), possibilities for enterprises to transfer this increase to consumer prices are limited and

they need to cover the increase in costs from their profit.

The logic and formulas of the GDP deflator breakdown

When breaking down the GDP deflator into components, national accounts data are used, specifically — real GDP and

components of nominal GDP calculated using the income approach.28

These components are income of various kinds,

such as compensation of employees, gross operating surplus and mixed income, as well as taxes:

P × Y = WIN + GOS + TAXN,

where: P ×Y — nominal GDP (P — GDP deflator, Y — GDP volume or real GDP), WIN — nominal compensation of em-

ployees, GOS — gross operating surplus and mixed income, TAXN —taxes (more precisely, difference of taxes, applied to

production and imports, as well as subsidies). The indicator of gross operating surplus and mixed income is considered to

be the measure most similar to profit, which can be obtained from national accounts, thus thereafter it will be called profit.

By dividing both sides of the presented identity from the GDP volume, unit (i.e. the production unit) indicators are ob-

tained. Thus, the price of the GDP unit (GDP deflator) is the sum of unit labour costs (ULC), unit profits (UGOS) and unit

taxes (UTAXN):

P = WIN/Y + GOS/Y + TAXN/Y = ULC + UGOS + UTAXN.

Since the GDP deflator may be broken down into unit components, its change may be explained by the changes of

these components.

Trends of unit components of the GDP deflator

The GDP deflator breakdown according to the data of Lithuania and the euro area is presented in Chart A. The chart

shows how the deflator level (2010 = 1) is changing and its distribution by components. It is obvious that the euro area

GDP deflator and its structure were more stable: the deflator increased consistently and the distribution of its components

remained broadly unchanged. As it is common in emerging market economies, wider fluctuations were characteristic to

Lithuania: the deflator increased faster during the economic upturn, declined during the recession and the distribution of its

components changed. The GDP deflator of Lithuania has a slightly different structure: unit profits comprise its largest part,

whereas the largest part in the euro area consists of unit labour costs.

_________________________________ 28

Seasonally and working day adjusted quarterly data are used.

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–2

0

2

4

6

8

10

–4

–2

0

2

4

6

8

10

2006–2008 2009–2010 2011–2015

Unit profits

Unit labour costs

Unit taxes

GDP deflator (right-hand scale)



Chart A. GDP deflator factors in Lithuania and in the euro area (ratio of components of nominal GDP, calculated using the income approach, to real GDP)

Lithuania Euro area

The components that determined the annual change of the GDP deflator in each quarter and the three periods

used29

are presented in Chart B. The chart shows not only higher volatility of the GDP deflator in Lithuania than in the

euro area, but also the fact that GDP deflator changes in Lithuania and the euro area were determined by different

factors.

Chart B. Contributions to GDP deflator changes in Lithuania and the euro area

Lithuania Euro area

In the period from 2006–2008, unit labour costs had a similar effect as unit profits in the euro area, whereas in Lithu-

ania their effect was noticeably stronger. Wages grew very fast in our country. Their growth was supported by the

bargaining power of employees, which increased due to the labour force shortage. Thus, compensation of employees

grew considerably more than real GDP and the ratio of these indicators — unit labour costs — grew rapidly as well. The

circumstances were favourable for the profit to grow during the economic upturn both in Lithuania and in the euro area:

after the rise in labour costs and in the presence of strong production demand, enterprises could not only maintain the

profit earned by transferring higher costs to production prices, but also increase it by raising prices more.

In 2009, the euro area GDP deflator continued to increase, supported by the growth of labour costs. It was supported

by labour productivity changes. When real GDP declined, enterprises did not rush to reduce the number of employees;

_________________________________ 29

The periods approximately coincide with pre-crisis, crisis and post-crisis periods. Such coincidence is conditional, since the economic recession in Lithuania and the euro area started and ended at different times; besides, the euro area also suffered the second recession (from the second half of 2011 to the beginning of 2013), which was not observed in Lithuania.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ratio, fraction of a unit


0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ratio, fraction of a unit

–10

–5

0

5

10

15

–10

–5

0

5

10

15

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015


–2

–1

0

1

2

3

4

–2

–1

0

1

2

3

4

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015


–1

0

1

2

3

–1

0

1

2

3

2006–2008 2009–2010 2011–2015


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therefore, productivity — the ratio of real GDP to the number of employ-

ees — declined. Still, the growth of the GDP deflator decelerated, since

the contribution of unit profits became negative. Unit profits acted as a

buffer: they slowed down the price growth when labour costs were

rising. Such effect was also present during the second recession in the

euro area: the contribution of unit profits was negative again in 2012 and

slowed down the accelerated growth of the GDP deflator.

As in the euro area, the impact of unit profits in Lithuania in 2009

was negative. However, a more important factor of the decline of the

GDP deflator was unit labour costs. Although labour productivity de-

clined, as in the euro area, the upward effect of such change on unit

labour costs was offset by a large decline of wages (see Chart C).

Summarising the factors of the GDP deflator change in 2009 and 2010,

it could be said that price adjustment in the euro area was more related

to unit profits, whereas in Lithuania — to the trends of unit labour costs,

more specifically the fall in wages.

In the period from 2011, which also includes the second recession in the euro area, the contribution of labour costs to

the change of the GDP deflator declined, the impact of profits was moderate and both were considerably lower than in the

period of 2006–2008. The impact of taxes increased, supported by fiscal consolidation measures and increasing produc-

tion prices.

Since 2011, economic growth has become more sustainable in Lithuania. The GDP deflator has been growing much

more slowly than before the crisis, since the contribution of both unit labour costs and unit profits has declined. In different

years of the post-crisis period the contribution of unit profits differed considerably: it was high and positive during the

recovery, later it declined and currently it is negative. The latter trend is new. The last time the contribution of profits was

negative was in 2009, in the middle of the crisis. Currently profits perform the above-mentioned role of the buffer in Lithua-

nia: owing to the lack of possibilities to raise prices (e.g. due to the environment of low inflation) and the increase in labour

costs (related to the shortage of the labour force of suitable qualification and to the minimum wage increase), enterprises

are forced to accept lower profits.

It is possible to perform even more detailed analysis of unit profits and unit labour costs in Lithuania by breaking down

the contribution of these indicators to the GDP deflator changes by economic activity. To ensure that the trends are more

obvious, quarterly data are aggregated to annual (see Chart D).

Chart D. Breakdown of contributions to the annual change of the GDP deflator by economic activity

Contribution of unit profits Contribution of unit labour costs

Chart D shows that the services sector, which is the largest economic sector, is the most significant. Still, there were

periods, when indicators of other sectors stood out. For example, in 2009, a strong negative contribution on unit profits was

exerted by construction — the economic activity that was most heavily affected by the crisis. Meanwhile, a strong positive

contribution of unit profits in 2010 and 2011 was mostly related to manufacturing. During that period, competitiveness of

Lithuanian exporters improved, imports grew and market shares widened due to wage developments that determined a

slow growth of exporters’ costs and due to the rise in labour productivity. Thus, manufacturing enterprises had opportuni-

ties to boost their profits. The negative contribution of unit profits in 2014 was affected by all sectors, except construction,

i.e. agriculture, industry and services. The development of construction in 2013 and 2014 was favourable to the increase of

profits by construction enterprises. This activity noticeably contributed to the economic growth, investment in industrial

–6

–4

–2

0

2

4

6

2006 2007 2008 2009 2010 2011 2012 2013 2014

Agriculture, forestry and fishing

Manufacturing

Industry, excluding manufacturing

Construction

Services

Unit profits

Sources: Statistics Lithuania and Bank of Lithuania calculations. Note: the difference between other taxes on production and other subsidies on production is not shown.

Percentage points

–4

–2

0

2

4

6

8

2006 2007 2008 2009 2010 2011 2012 2013 2014

Agriculture, forestry and fishing

Manufacturing

Industry, excluding manufacturing

Construction

Services

Unit labour costs

Sources: Statistics Lithuania and Bank of Lithuania calculations

Percentage points

Chart C. More detailed breakdown of GDP deflator changes in Lithuania

–10

–5

0

5

10

15

–10

–5

0

5

10

15

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Labour productivity (with opposite sign)

Compensation per employee

Unit profits

GDP deflator (right-hand scale)

Sources: Statistics Lithuania and Bank of Lithuania calculations. Note: for the sake of simplicity, the contribution of unit taxes is not shown.

Percentage points Percent, annual change

Unit labour costs

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constructions and warehouses recovered and the residential real estate market became more active.

In addition to the GDP deflator structure and contributions to its growth, the level of unit components is an informative

indicator, since it allows observing quarterly trends and establishing which components declined more during the crisis

or recovered faster after it. Chart E shows that unit components of the GDP deflator of Lithuania declined fast during the

crisis, whereas their changes, especially those of unit labour costs, were more moderate in the euro area.

Chart E. Developments of unit components of the GDP deflator

Lithuania Euro area

Both in Lithuania and in the euro area unit profits, unit labour costs and unit taxes increased after the crisis, albeit at

a different rate. In the euro area, all GDP deflator components currently exceed their highest pre-crisis level. The in-

crease in unit labour costs is particularly noticeable; however, unit profits are not much higher than prior to the crisis;

moreover, they reached the pre-crisis level quite late — at the beginning of 2011. In Lithuania, the situation is opposite:

the recovery in unit labour costs is slower than in unit profits. Unit labour costs still remain (after the increase that lasted

more than 4 years) slightly below the highest pre-crisis level. Naturally, that level was one of the signs of the overheating

of the economy; therefore, it is unreasonable to expect that Lithuania will exceed it significantly in the near future.

In Lithuania, unit profits reached the pre-crisis level early, i.e. already

at the beginning of 2010, and fast — over one quarter. When talking

about such fast recovery, we should remember that the unit profit devel-

opment is related to the differences in the changes of two indicators used

in its calculation (gross operating surplus and mixed income as well as

real GDP). The indicator of gross operating surplus and mixed income

reached the pre-crisis level only in the middle of 2011 (see Chart F), a

year later than its ratio to real GDP (unit profits). This ratio reached the

former level so fast due to the fact that the indicator of gross operating

surplus and mixed income declined much more than real GDP during the

crisis; therefore, it grew much faster than real GDP after the crisis (real

GDP reached the pre-crisis level only in the middle of 2014).

Other profit indicators calculated from national accounts

The discussed unit components of the GDP deflator are closely relat-

ed to other profit and labour income indicators calculated from national accounts data. When the latter are known, it is

also possible to calculate the discussed unit indicators. For example, unit profits may be calculated by multiplying the

profit share, compared to nominal GDP, by the GDP deflator:

UGOS = GOS/(P × Y) × (P × Y)/Y.

Accordingly, it is possible to calculate unit labour costs by using the share of labour income. Labour income and prof-

it shares show the distribution of income to factors of production, i.e. labour and capital.

Profit share is similar to the accounting indicator of profitability of enterprises — the ratio of profit to sales income.

Meanwhile, the labour income share has an important purpose when conducting economic research. According to the

economic theory, enterprises optimise their activity by setting prices, which correspond to marginal costs increased by a

certain value. Marginal costs cannot be observed; however, they may be replaced by economic indicators that have

similar meaning. When applying the New Keynesian Phillips curve, unit labour costs are considered to be the estimate of

nominal marginal costs, whereas the labour income share is considered to be the estimate of real marginal costs.

Although unambiguous results are not obtained, similar trends of the labour income share and inflation may indicate

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

GDP deflatorUnit profitsUnit labour costsUnit taxes

Index, Q1 2005 = 1

Sources: Eurostat and Bank of Lithuania calculations. Note: lighter horizontal lines represent the highest level of respective indicators in the period 2005–2009.

0.95

1.00

1.05

1.10

1.15

1.20

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Index, Q1 2005 = 1

Chart F. Levels of profit and real GDP

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Gross operating surplus and mixed income

Real GDP

Index, Q1 2005 = 1


42

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/

De

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mb

er

20

15

causality: when the market structure and technology do not change, a

large labour income share means lower than acceptable for enterprises

profit mark-up and thus induces inflation.

The profit and labour income shares in Lithuania are currently at their

long-term average (see Chart G). Both shares approached their averag-

es just recently. In 2011–2013, they were quite stable, although deviated

from their averages, whereas from the beginning of 2014, the profit

share declined consistently and the labour income share increased.

According to the data of the middle of 2015, profit and labour income

shares are the same as in the beginning of 2006. The situation is not as

tense as it was during the economic upturn, when wages, with a lack in

workers, rose substantially faster than nominal GDP and the labour

income share was considerably higher than now. The current increase in

the labour income share is probably related to improving situation in the

labour market and increasing bargaining power of employees: although

the unemployment is still substantial, there is a shortage of employees

who have suitable qualification and this creates pressure on the wage

growth.

Profit mark-up for the total economy could be approximately evaluat-

ed calculating the ratio of the GDP deflator to unit labour costs, i.e. the

ratio of nominal GDP to compensation of employees. This indicator is

opposite to the labour income share. The ratio of nominal GDP to

compensation of employees may be explained as the amount of nominal

GDP euros per euro paid in the form of compensation. After subtracting

1 from this ratio and calculating it as a percentage, we obtain the per-

centage that shows the mark-up that enterprises are able to add to

marginal costs.

However, usually it is not the profit mark-up that is used for analysis,

but its annual change calculated as the difference of annual changes in

the GDP deflator and in unit labour costs. Chart H shows the change of

profit mark-up and reveals the same trends as Chart B, which reflects

the contribution of unit components to the GDP deflator change. Same

as with unit profits, the profit mark-up is currently decreasing, which

determines the decline in the GDP deflator.

As was mentioned before, the development of profit indicators de-

pends on the capability of enterprises to adjust their production prices in

line with the changes of production costs. Sometimes they cannot

transfer higher costs to consumers and have to accept those costs

themselves; therefore the profit earned by them decreases. The capacity

of enterprises to increase production prices is highly dependent on the

overall economic situation, for example, on demand trends. The latter

are reflected by such indicators as theoutput gap or the production

capacity utilisation level. Data for Lithuania also confirms this link. Chart

I shows that the trends of profit indicators are quite similar to those of

the production capacity utilisation level. The growth of gross operating

surplus and mixed income — the indicator that is not calculated as the

ratio to GDP — is the most similar to the trends of the capacity utilisation

level.

Deficiencies of profit indicators calculation from national accounts

The calculation of profit indicators from national accounts has its shortcomings. They are related to the peculiarities of

gross operating surplus and mixed income indicator, which is used in the calculations. This indicator is the measure most

similar to profit that can be obtained from national accounts; however, it also has important differences.

First, gross operating surplus and mixed income cover not only profit, but also certain other income. It is essentially a

residual value: it covers everything that is not income of employees and taxes. As we can see from the name of the indica-

tor, it covers not only gross operating surplus,but mixed income as well.30

The latter is the income that cannot be attributed

_________________________________ 30

For each of these components, annual data is provided; however, quarterly data, which would be most useful for the analysis of the latest trends, is not provided.

Chart G. Profit and labour income shares compared to

nominal GDP

Chart H. Developments of the GDP deflator, unit labour costs and profit mark-up

Chart I. Profit indicators and the developments of produc-tion capacity utilisation level

37

39

41

43

45

47

49

51

53

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Share of gross operating surplus and mixed income

Labour income share

Per cent

Sources: Eurostat and Bank of Lithuania calculations. Notes: 1) horizontal lines represent the long-term average of respective indicators; 2) the share of taxes is not shown.

–15

–10

–5

0

5

10

15

20

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

GDP deflator

Unit labour costs

Profit mark-up



–24

–16

–8

0

8

16

24

32

–6

–4

–2

0

2

4

6

8

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Contribution of unit profits to the annual change of the GDP deflator

Profit mark-up (right-hand scale)

Gross operating surplus and mixed income (right-hand scale)

Production capacity utilisation level (right-hand scale)


Sources: Eurostat, European Commission and Bank of Lithuania calculations.

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to labour or capital only, for example, the income of sole proprietorships

and self-employed persons. Data on income of self-employed persons

is not published; however, in order to purify the profit indicator, this

income may be estimated from the data of compensation of employees

and then subtracted from profit and attributed to labour income. When

estimating income of self-employed persons, an assumption is made

that compensation to one such person is the same as to one employed

person. By multiplying this compensation by the number of self-

employed persons, the estimate of compensation to the self-employed

is obtained. When performing calculations, income of self-employed

persons is not included in labour income, since results and conclusions

remain substantially unchanged (see Chart J) and uncertainty about

approximate size of this income is avoided.

Second, gross operating surplus and mixed income also cover such

components that are not covered by the usual concept of profit. For

example, fixed capital consumption — the decline in the value of

available fixed assets due to wear, ageing or accidental damage — is

included. Fixed capital consumption in Lithuania in the period of 2005–

2014 comprised on average around one fourth of gross operating

surplus and mixed income. It can be subtracted from the overall indica-

tor to obtain the respective net indicator (simply speaking — net profit).

By dividing the latter from real GDP, net unit profits are obtained. Their

development trend in Lithuania is very similar to that of gross unit

profits (see Chart K). Thus, the results of the analysis remained virtually

unchanged due to the calculation of net profits, same as due to the

inclusion of income of self-employed persons in labour income.

Conclusions

The influence of domestic factors on prices in Lithuania may be es-

timated by breaking down the changes of the GDP deflator into contributions of unit components — unit labour costs,

unit profits and unit taxes. They fluctuated considerably in the last decade. During the economic upturn, wages and unit

labour costs grew very fast due to the labour force shortage. The growth of the GDP deflator in that period was also

supported by profits, since favourable conditions for its increase were created by strong domestic demand. During the

economic recession, unit labour costs declined substantially due to a considerable decline in wages. In the period from

2011, when the economic growth became more sustainable, prices grew slower than before the crisis, since the positive

contribution of both unit labour costs and unit profits declined.

The trend of unit profits changed from 2014 — they are declining and their contribution to the GDP deflator growth is

negative. Thus, profits perform a role of a buffer, i.e. they slow down the price growth. Enterprises have to accept lower

profits as labour costs are increasing and in the environment of low inflation there are not many possibilities for raising

prices. The growth of the labour costs is currently determined by such factors as the shortage of the labour force of

suitable qualification and the minimum wage increase. Nevertheless, even now, after the long-lasting increase, unit

labour costs remain slightly below the highest pre-crisis level.

Other indicators that are calculated from national accounts — profit mark-up, profit and labour income shares —

show similar trends as unit components of the GDP deflator. For example, same as unit profits, the profit mark-up is

currently decreasing, which determines the decline in the GDP deflator. The labour income and profit shares that reflect

income distribution to factors of production are currently close to their long-term averages. However, the profit share has

been declining recently, whereas the labour income share has been growing. The latter share remains considerably

lower than during the economic boom, when wages grew much faster than nominal GDP. However, its increase shows

that the bargaining power of employees increases: although the unemployment is still substantial, there is a shortage of

the labour force with suitable qualification and this creates pressure on the wage growth.

Chart J. Contributions to the change of the GDP deflator in Lithuania, showing the impact of the inclusion of income of self-employed persons in labour income

Chart K. Unit profits and unit fixed capital consumption

–10

–5

0

5

10

15

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Unit profits after subtracting the share attributable to unit labour costs

Unit profit share attributable to unit labour costs

Unit labour costs


Percentage points

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Gross unit profitsUnit fixed capital consumptionNet unit profits

Index, Q1 2005 = 1


lithuanian economic review 2015 · 2015 december 2015 lithuanian economic review . author: rasa...

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