Baltic Journal of Economics - BICEPS

The Baltic Journal of Economics

Jorgen Drud HansenEditor in Chief

University of Southern Denmark and EuroFaculty, University of Vilnius

Raul EametsEditor

University of Tartu

Mihails HazansEditor

University of Latvia

Daunis AuersManaging Editor

EuroFaculty, University of Latvia

The Baltic Journal of Economics (ISSN-140X-099X) is published twice a year in December and July by EuroFaculty, Raina Blvd. 19, Riga LV-1586, Latvia.

Information for subscribers: Copy requests, orders and other enquiries should be addressed to the BJE at the address above, or by email to [email protected].

The Baltic Journal of Economics is published by EuroFaculty and refereed by internationally recognized scientific standards. The journal is intended to provide a publication medium for original research in economics for scholars working in the Baltic states or those who are working on topics relevant to the Baltic states. Papers may be theoretical, empirical or political economy in emphasis. Papers with policy relevance or which combine economic theory with empirical findings are particularly welcome. The Journal aims to stimulate a dialogue between scientists in social science, policy makers as well as other decision makers involved with economic development in the Baltic States. In order to make the journal relevant to a wide audience of academics trained in social science the articles should be presented in a form where explanations and the intuition behind the conclusions should be given priority above technical derivations.

For more information about the journal see the website: www.eurofaculty.lv/bje

� Copyright EuroFaculty 2002 ISSN-140X-099X

Contents

1 ForewordGustav Kristensen

Articles

3 Some Unpleasant (and then Some Pleasant) Transition ArithmeticRobert Elder

8 Financing Constraints as Determinants of the Investment Behaviour of EstonianFirmsJaan Masso

31 Gender Wage Differences in Soviet and Transitional EstoniaCharles Kroncke and Kenneth Smith

50 The Outward Expansion of the Largest Baltic Corporations – Survey ResultsKari Liuhto & Jari Jumpponen

Book Reviews

76 Bertola G, Boeri T and Nicoletti G (eds) Welfare and Unemployment in a United Europe: A Study for the Fondazione Rodolfo Debenedetti (MIT 2001)Alf Vanags

79 Keuschnigg, M Comparative Advantage in International Trade: Theory and Evidence (Physica-Verlag 1999)Alf Vanags

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FOREWORDThe first volume of the Baltic Journal of Economics (BJE) was published byEuroFaculty in 1997, the second volume following in winter 1999. There thenfollowed a lull in activity. However, the growing need for a prestigious Baltic-basedrefereed economics journal called for the renewal of the organization of the BalticJournal of Economics. A high technical standard, stable financing, and stableprofessional leadership has been secured through EuroFaculty Baltic resources andthe generous support of our sponsors.

The Baltic scientific capacity in the field of economics is currently quite small. Thisis the strongest argument for cooperation around one common scientific journal ofeconomics for all three Baltic states. It is the goal of the BJE to raise the Baltic statesscientific capacity in economics, over a number of years, to the level of comparablysized European countries e.g. Denmark, Sweden and the Netherlands

Thus the function of the Baltic Journal of Economics is threefold: (i) to encourageBaltic scientists in their economics research by giving them a potential medium forrefereed publication; (ii) to create a network of national and international refereeswho, through interaction with Baltic researchers, will increase the scientific level inthe Baltic states; and (iii) to disseminate economics research on the Baltic Statesthrough the distribution of free copies of the BJE to academic and researchinstitutions globally and the creation of an open web-site featuring all past andfuture editions of the journal.

Gustav KristensenDirectorEuroFaculty-Tartu-Riga-Vilnius

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Some Unpleasant (and then Some Pleasant) Transition Arithmetic

Some Unpleasant (and then SomePleasant) Transition ArithmeticRobert Elder1

Twenty-one years ago, Thomas Sargent and Neil Wallace wrote an article entitled“Some Unpleasant Monetarist Arithmetic” (1981). I am no Monetarist, but I do payattention to what Monetarists say, and I cheerfully acknowledge that these twoMonetarists thought up a clever title for the paper they wrote for the MinneapolisFed’s Quarterly Review back in 1981. Here in 2002, the subject I address in theparagraphs below is not Monetarism, but instead the experience of transitioneconomies. In particular, I call attention to some arithmetic that can help organizeour thoughts with regard to some of the hardships a country endures and some ofthe successes a country achieves during the transition from central planning to freemarkets. I focus on equations involving important stocks and flows of people insuch a country in transition, subsequently citing data on those stocks and flowsbetween the years of 1992 and 2000 here in Latvia.

When analyzing an economy in transition, a good place to start is with anequation describing the division of employment (E) between the state sector (ES)and the private sector (EP).

E = ES + EPEach of the three magnitudes in this equation is a stock. At any moment in time, forexample, we can observe the quantity of people with jobs (the stock of employmentE), the quantity of people working for the state (the stock of state sectoremployment ES), and the quantity of people working for private firms (the stock ofprivate sector employment EP). This equation can also be termed static, since itdescribes the state of employment at any given moment in time. If we move forwardfrom one moment in time to some subsequent moment in time, we can make theequation dynamic, observing how these stocks change as time elapses.

ΔE = ΔES + ΔEP

Each of the three magnitudes in this equation is a flow. Positive flows add to stocks,while negative flows subtract from stocks. (For example, the Daugava River thatruns through Riga is a flow that adds to the Baltic Sea, a stock.) Equipped with the

1 Professor of Economics, Beloit College ([email protected]), and for the 2001-2002 academic year,Fulbright Scholar in the Eurofaculty program at the University of Latvia ([email protected]). Thisessay benefits from valuable suggestions that Mihails Hazans, my colleague at the University of Latvia,made on earlier drafts, and I am grateful to him for his helpful comments.

Baltic Journal of Economics Autumn/Winter 2002

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flow equation shown above, we can start to highlight a key element of transition.During the transition from central planning to free markets, there is a flow ofemployment out of the state sector (ΔES < 0) and into the private sector (ΔEP > 0).If each worker leaving the state sector found work in the private sector, note thatΔEP = – ΔES, so from the flow equation above ΔE = 0, and there would be no changein aggregate employment. In this event, the sectoral reallocation of labor associatedwith transition might appear relatively painless. But transition is not that easy, andindeed can be painful, because the outflow from state sector employment does notnecessarily result in an equal inflow into private sector employment. Overall, if ΔEP< – ΔES, then ΔE < 0, and aggregate employment can fall during the transition.Thus, the flow out of state sector employment might not lead entirely to a flow intoprivate sector employment but also to a flow into unemployment. To bringunemployment into the arithmetic, we begin once more with an appropriate stockequation. At any given moment in time, the economically active segment of thepopulation (EA) consists of those who have jobs (in the state sector, ES, and in theprivate sector, EP) and those who seek jobs (the unemployed, U):

EA = ES + EP + U.

Setting this equation into motion by allowing time to elapse, the associateddynamic expression would be

ΔEA = ΔES + ΔEP + ΔU.

From this flow equation, we can verify the fact that for an economically activepopulation of constant size (ΔEA = 0), a flow out of state sector employment (ΔES< 0) can imply a flow into private sector employment (ΔEP > 0) as well as a flowinto unemployment (ΔU > 0). But unfortunately, the unpleasantness of state sectorshrinkage does not stop with rising unemployment. To see why, observe first thatthe assumption of no change in the size of the economically active population (ΔEA= 0) is unrealistic, and we can relax this assumption in our final pair of stock andflow equations. Starting once more with the pertinent stocks, note that we candichotomize the entire population (POP) between the economically active (job-holders, ES and EP, and job-seekers, U) and the economically inactive (EI):

POP = ES + EP + U + EI.

The associated flow equation follows as:

ΔPOP = ΔES + ΔEP + ΔU + ΔEI.

Here, for a given population (ΔPOP = 0), note that a flow out of state sector em-ployment (ΔES < 0) can imply (1) a flow into private sector employment (ΔEP > 0),(2) a flow into unemployment (ΔU > 0), and (3) a flow into economic inactivity(ΔEI > 0). Unpleasant dimensions of a flow into economic inactivity are apparentwhen any such flow involves involuntary retirement. And finally, when we

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acknowledge that the assumption of a constant population (ΔPOP = 0) isinappropriate to transition scenarios, we should add that a flow out of state sectoremployment can imply (4) a flow out of the population (ΔPOP < 0). From thesomewhat unpleasant to the definitely unpleasant, such flows out of the populationcan range from the temporary life disruptions of emigration to the permanent lifeterminations of death. As shown by the data below, emigration in excess ofimmigration and deaths in excess of births have been a steady feature of thetransition years from 1992 through 2000 in Latvia2. As it chronicles these nineconsecutive years of population decrease, notice that the table reveals netemigration as the larger source of population decrease during the first three yearsand net deaths as the larger source of population decrease during the final six years.

Table 1: Sources of Population Change in Latvia, 1992-2000(in thousands)

The next table allows us to monitor the behavior of our final stock equationin Latvia during these same years.

Table 2: POP = ES + EP + U + EI, Latvia, 1992-2000 (in thousands)

Here in Table 2, the first thing that we see is the monotonically decreasing

2 Observe that these years do not span the entire transition, which could be dated from the redeclarationof Latvia’s independence in May 1990 through the present. I focus on 1992 through 2000 because theseare the years for which there exists available data for each of the variables that I have introduced in thepreceding discussion.


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population implied by Table 1. Also readily apparent from this table of stockmagnitudes are two distinguishing features of transition: monotonicallydecreasing state sector employment and monotonically increasing private sectoremployment. In contrast, as shown by the final two columns of data, the stocks ofthe unemployed and the economically inactive display non-monotonic behavior,decreasing during some years and increasing in others. To organize our thoughtsabout the behavior of the five stocks recorded in Table 2 and to examine thetransition from a different angle, I use the stock data provided by Latvia’s CentralStatistical Bureau to construct the table pertinent to our final flow equation. Theresults appear in Table 3. Since each stock in Table 2 is an annual average, each flowin Table 3 is the difference between each pair of consecutive annual averages, asindicated below.

Table 3: ΔPOP = ΔES + ΔEP + ΔU + ΔEI, Latvia, 1992-2000(in thousands)

To think about what’s going on in the first four rows of the table, observe first thatwe can re-express our final flow equation as follows:

– ΔES – ΔEI + ΔPOP = ΔEP + ΔU.

For the period from 1992 to 1996, this re-expression of our final flow equationgroups outflows on the left-hand side and inflows on the right-hand side. Duringthese years, this equation says that the sum of outflows from state sectoremployment, the economically inactive, and the population were equal to the sumof inflows into private sector employment and unemployment. For example, from1992 to 1993 the observation ΔES = – 170 implies – ΔES = 170, an outflow of170,000 people from state sector employment available as a source of inflows intoprivate sector employment and unemployment. Similarly, the 1992 to 1993observation ΔEI = – 25 implies – ΔEI = 25, an outflow of 25,000 people comingfrom the ranks of the economically inactive to provide a second source of inflowsinto private sector employment and unemployment. In contrast, the 1992 to 1993observation ΔPOP = – 52 remains negative on the left-hand side of this equation,since an outflow of 52,000 people from the population diminished what wasavailable to facilitate inflows into private sector employment and unemploymentbetween these years. To highlight the leading destinations of workers leaving thestate sector, notice that the while private sector employment absorbed the largest

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share of shrinking state sector employment from 1992 to 1993, unemploymentabsorbed the largest share of shrinking state sector employment from 1993 to 1994,and the population absorbed the largest share of shrinking state sector employmentin 1994 to 1996.

Following 1996 to 1997, which was unique for its flow into economic inactivity, wemove to the final three years from 1997-2000, during which the outflows-equals-inflows equation becomes

– ΔES – ΔEI + ΔPOP – ΔU = ΔEP.

For each of these last three years, this equation features four outflow terms on theleft and one inflow term on the right. Happily, note that the only stock absorbinginflows during these last three years is private sector employment. Althoughoutflows from the population reduce what could have been additional inflows intoprivate sector employment, positive contributions to these recent years of inflowsinto private sector employment are made by outflows from state sector employmentas well as outflows from the ranks of the economically inactive and the unemployed.

This is a positive point with which to conclude. Overall, the subject ofeconomic transition is sprawling, and there are many aspects of it left unaddressedin this brief note. Further, there are many countries involved in economictransition, and a comparison between their experiences and those of Latvia wouldprovide additional perspective. As we have seen here in Latvia, the early years oftransition can feature some unpleasant dynamics, but as we have also seen morerecently, more pleasant dynamics can follow.

References

Central Statistical Bureau of Latvia, Demographic Yearbook of Latvia 2001.

Central Statistical Bureau of Latvia, Statistical Yearbook of Latvia 2001.

Sargent, T.J., and Wallace, N (1981), “Some Unpleasant Monetarist Arithmetic,”Federal Reserve Bank of Minneapolis Quarterly Review, Volume 5, Number 3,pages 1-17.


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FINANCING CONSTRAINTS ASDETERMINANTS OF THE INVESTMENTBEHAVIOUR OF ESTONIAN FIRMS1

Jaan Masso2

AbstractLack of financing is arguably the main obstacle for making profitable investmentsin transition economies. In this paper we investigate whether there isunderinvestment due to financing constraints in Estonian manufacturing firms.Firm level panel data from 1995 through 1998 with several items from financialstatements were used. The unique data includes many very small firms with assetsless than 1 million USD that are often not explored in empirical studies. Theexistence of liquidity constraints was tested with estimating regression coefficientsof inside - firm financing from reduced form investment regressions and using theinvestment Euler equation. Results show that internal finance played a bigger rolefor investments made by small firms and firms owned by domestic (non-foreign)capital. The only exception is that in the Euler equation, cash flow influencedinvestments more for small than for large foreign firms. The results imply thatforeign direct investments and lower corporate income taxes can promoteinvestments through the relaxation of liquidity constraints.

JEL classification: G31, E22Keywords: Financing constraints; Investment; Cash flow; Estonia

1 IntroductionSince the seminal article by Fazzari et al. (1988) numerous papers discuss theeffects of financing conditions on the investment decisions of private firms. Inseveral papers, it has been found that investment is more sensitive to the availabilityof internal funds among certain groups of firms that are more subject to thepresence of information and agency problems in financial markets. These include,among others, small and young firms, firms without credit ratings, and firmswithout affiliation to an industrial or banking group. The presence of financing or

1The author is grateful for many helpful comments and remarks from Riku Kinnunen, threeanonymous referees, the editor and participants of seminars held in Stockholm, Tartu and Tallinn thathave significantly contributed to the quality of the paper. I am also obliged to Urmas Varblane forgenerously providing the data that was used in this study. All remaining errors are of course my soleresponsibility. 2Faculty of Economics and Business Administration, University of Tartu, Estonia. E-mail address:[email protected]

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Financing constraints as determinants of the investment behaviour of Estonian Firms

liquidity constraints (in the paper both notions are used interchangeably) hasseveral implications for tax policy, corporate take-overs and the channels ofmacroeconomic policy (Hubbard, 1998).

There is a widespread view among economists that capital marketimperfections are especially severe in Central and Eastern European transitioneconomies (Coricelli, 1996). This is because many firms in the new marketeconomies are newly established without credit history, track record, collateral etc.Also, the weakness of the banking sector creates problems due to the banks’inexperience in monitoring and gathering information about loan applicants.Economic uncertainty has lead to an unwillingness or inability among the banks tolend long-term (Pissarides, 1998). On the demand side, firms need to invest heavilyin order to modernize obsolete capital stock and increase competitiveness in worldmarkets. Thus, the lack of financing probably constitutes one of the main obstaclesto growth.

There have not been many studies on this topic in the transition economies,mainly due to a lack of enterprise-level data. Perotti and Gelfer (1998) have shownthat investment in firms belonging to financial-industrial groups in Russia is lesssensitive to cash flow than investment in independent firms. On the other hand,Lizal and Svejnar (1998) did not find evidence of a positive link between internalfinance and gross investment, although in their later study (2000) of net investmentretained profits were shown to have positive effect. Anderson and Kegels (1997) alsofound evidence of the influence of financial variables like cash flow, beginning-of-period bank debt and trade credit on the fixed investment of Czech enterprises.Bratkowski et al. (1999) argue that imperfections in capital markets in CentralEuropean economies do not seem to affect the growth of new private firms. ForBulgaria, Budina et al. (2000) found liquidity constraints to be important for smallfirms but not for large. This finding was explained by the inefficiency of the financialsector because of loans granted to large unprofitable firms. One weakness of thesestudies is that their inferences have been based on reduced form investmentregressions, rather than explicit conditions of optimal capital accumulation.

In the present paper, we try to see whether firm size and ownership areimportant in determining whether Estonian industrial firms can finance profitableinvestment projects. We succeeded in getting access to enterprise level panel datathat allows an examination of how the severity of financing constraints varies acrossdifferent types of firms. Also, in this way the aggregation bias can be avoided. Thuswe focus only on fixed investments; however financing constraints could influencealso investments in inventories, research and development, market share etc.(Hubbard, 1998). The existence of liquidity constraints was tested in two ways.First, we estimated simple reduced form investment regressions in order to observe


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whether internal finance affects investment positively. Second, we used theinvestment Euler equation derived from the objective of maximizing a firm’s valueunder convex adjustment costs and constant returns to scale. The results from bothapproaches show that the availability of internal finance plays a significantly biggerrole for investments of small and domestically owned private companies. The policyimplication of the results is that recent changes in taxation law in Estonia that havemade taxation more favourable for retained earnings than for dividends, promoteinvestment of the aforementioned firms. Also, the range of benefits for attractingforeign direct investments into the country can be seen more widely - alongsideother positive effects foreign direct investments could also loosen liquidityconstraints.

The remainder of the paper is organized as follows. Section 2 provides somestylized evidence of investment and financing behaviour of Estonian firms. Section3 describes the dataset of Estonian manufacturing firms. Section 4 describes themethod and results of the reduced form investment equations and section 5 thoseof the more elaborate Euler equation. The final section concludes with discussion ofthe findings and policy implications.

2 Stylized facts of the investment and financing problems of EstonianfirmsBefore a formal statistical analyses we will present some stylized evidence about theinvestment and financing behaviour of Estonian firms. First, the Estonian Instituteof Economic Research has conducted inquiries among Estonian firms about theirinvestment decisions (Konjunktuur, 1999). Table 1 summarizes the relevance ofdifferent factors limiting investment.

As we can see, in all years the biggest obstacle for investment has been asmall profit. On the one hand this could simply reflect a low internal rate of returnin comparison to the cost of capital, as noted by Raudsepp and Leoshko (1999). Buton the other hand this could also show a tendency to mostly rely on internal fundswhen carrying through investments. Many studies in developed economies showinternal finance or cash flow to be the primary source of funds, e.g. Fazzari andPetersen (1993) found that cash flow constitutes 71 % of net sources of finance forUS public firms paying dividends less than 10 % of earnings. For Estonia it has beenargued that internal financing constitutes a smaller part of funds than in developedcountries because of a lack of internal funds and unstable economic development.For instance, Kangur et al. (1999) estimated that about 49 % of total investment wasinternally financed.

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Table 1 Factors limiting the investments of manufacturing firms in Estonia

(percent of enterprises surveyed)

The high cost of capital is often considered an obstacle (up to 32 % notedthis as a problem). For example, in a survey among firms in the United Kingdomonly 6-10 % viewed the cost of finance as a limitation to capital expenditures (Bondand Jenkinson, 1996). This finding for Estonian firms could result from the costdisadvantage of external funds due to high transaction costs, agency costs andasymmetric information. Earlier papers on the financial problems of Estonian firmshave found the cost of capital to be too high, especially for small and medium sizeenterprises (Raudsepp and Leoshko, 1999). Difficulties in obtaining credit (19-31 %of respondents) could possibly reflect credit rationing, i.e. some applicants aredenied loans in spite of their readiness to carry all the price and non-pricecomponents of the loan contract (see Stiglitz and Weiss, 1981) or difficulties withnecessary collateral, both of which are consistent with an imperfect capital marketstory. Similarly, in the aforementioned study of UK firms only 2-3 % of firmsreported an inability to raise external finance as a problem (Bond and Jenkinson,1996).

We could infer that financial factors seem to constrain capital investmentsmore in transition economies than in developed western economies. In particularthe availability and price of external (new debt and equity) versus internal financing(internally generated cash flow) is an issue. According to the financing hierarchyhypothesis firms prefer to use internal financing due to asymmetric informationbetween managers and potential new equity investors or creditors; external fundsare only used after internal sources are exhausted (Fazzari et al., 1988). One surveyamong Estonian non-financial firms listed in the Tallinn Stock Exchange showedthe presence of a financing hierarchy – internal equity was ranked as the mostpreferred source of financing (Raudsepp et al., 2000). Low dividend payout rates(on average 10 %) confirm this finding (Ibid.) because the cost disadvantage ofexternal funds forces firms to retain profits inside the firm (Fazzari et al., 1988a).The amount of internal financing may be constrained by relatively small rates of


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depreciation – in some sectors (machinery, chemical industry) these are 2 to 4times smaller than in European firms (Pärn and Lumiste, 2000). This may havesome negative effect on the amount of internal finance available due to a smaller taxshield of depreciation.

The Bank of Estonia carried out a study on the financing of the Estonianentrepreneurial sector during 1994-1998 as well as connections between financingand investment (see Kangur et al., 1999). The authors argued that aggregate datashows co-movement between external financing and total investment (investmentsin fixed assets plus), so that (external) financing can be an important factoraffecting the level of investments.

3 Data and summary statisticsIn the present study we used firm-level financial statements panel data collectedand compiled by the Statistical Office of Estonia. The original dataset includes 373industrial enterprises for the period 1995-1998. Only firms in manufacturingindustries were considered. Here manufacturing firms are those with 2-digitEMTAK codes between 15 and 39 that correspond to section "D" of European UnionNACE classification; these codes also correspond to SIC codes between 20 and 39.The firms in the sample represent about 70 % of the total sales of manufacturingindustry. The total number of firms in the industry in this period was about 4500(Statistical Yearbook of Estonia 2000), so the sample is biased towards large ratherthan small firms. When we consider that financing constraints can prevent businessfrom starting (see Evans and Jovanovic, 1987), so that some survivorship bias isintroduced, it can be suggested that the present study will tend to underestimaterather than overestimate the importance of financing constraints.

Several firms were deleted from the sample. First, all firms with negative orzero fixed tangible assets were deleted. Second, the possible effect of outliers onregression estimates was controlled by excluding firms with observations of salesgrowth, investment to capital ratio or cash flow to capital ratios below or above 5%upper and lower tails of distribution. The number of firms left is 195. Thejustification for excluding firms with extreme growth rates in sales or investment isthat if both investment and cash flow grow at a rate similar to growth rate of sales,then part of the co-movement could be due to the scale factor. This effect would biasthe estimates of investment-cash flow sensitivities towards one, particularly infirms with higher annual growth rates (Kaplan and Zingales, 1997).

Table 2 presents summary statistics for some of the regression variables aswell as the relative importance of different sources of finance for different subsamples of firms (the three last rows of the table). First, the total sample was splitinto three equally sized groups by the average value of real assets. As we can see

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from table 2, small and medium sized enterprises grow faster and invest more, sothe need for extra financing is greater. As expected, cash flow plays a bigger role asa source of financing for smaller firms. Both cash flow and investments are morevolatile for smaller firms. In earlier studies other researchers have found the sameevidence for classes of a-priori constrained firms (Fazzari et al., 1987). Only for the3rd group is new equity an important source of funds. In total (last column of table2) firms have been investing quite actively (average investment to capital ratio0.40). This has been largely financed by cash flow. Still the relative importance ofcash flow is somewhat smaller than in studies made with developed countries’ data,e.g. Fazzari and Petersen (1993) estimated the average cash flow to the net sourcesratio to be 0.715.

We can also see that firms belonging to foreign capital are on average muchbigger in terms of total assets and capital, and grow faster. The first evidence can beexplained by the fact that Estonian residents do not have enough capital (neithercould they borrow the funds) to privatize large state-owned firms. Both investmentsand cash flow are more volatile for domestic firms. Foreign firms also gotremarkably more new equity capital, which indicates their better access to externalfinancing. Here the firm is defined as belonging to foreign or Estonian capital if inall years (1995-98) more than 50 % of the share capital belonged to foreign orEstonian residents.

Table 2 Means of selected variables: sample of 195 manufacturing firms, period

1995-1998 (sample split by average value of real assets and form of ownership)


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4 Evidence of the existence of liquidity constraints from reducedform investment equationsThe existence of liquidity constraints is usually tested by regressing the investmenton variables that measure the availability of financing generated inside the firm andsome proxy for the investment demand (affected by productivity of capital,expectations, required rates of return). Often in the part of the latter is Tobin’s qthat theoretically should capture all relevant information and is basically the ratioof market value of firm’s equity and debt to replacement value of assets.3

Unfortunately the firms in the current sample are not listed on the stock market, sowe are unable to calculate such a measure. Instead we use employment growth tocontrol for the existence of investment opportunities, as with Bratkowski et al.(2000). Second, in place of liquidity variables cash flow and cash stock are used.The liquidity variables proxy for internal net worth (liquid assets plus thecollateralizeble value of illiquid assets), while they also convey information aboutwhat proportion of investment spending can be internally financed (Schiantarelli,1996). Firms with a higher level of liquidity can better collateralize debt issues andreceive loans at lower interest rates as well as exploit more relatively cheap internalfunds. It means that we are testing whether internal and external financing areperfect substitutes or not. The expected impact of cash flow and cash stock oninvestment is positive. The intuition for including the leverage variable is thatagency costs occurring due to diverging interest of lenders and borrowers (e.g.monitoring and bankruptcy costs) are assumed to increase in the amount of debtused. A higher level of debt in the beginning of the period makes it more difficult tofinance new investment projects, if there is a limit to the debt a firm can have. Sowe estimated the following equation:

(4.1)

where I denotes gross investment, LGROWTH employment growth measured inlogarithms, CF cash flow, CS cash stock, K capital stock and DEBT/A is the ratio ofshort- and long-term debt to total assets.4 The intercept coefficients, γi and γt allowfor firm specific and year intercepts; uit is random error term. Firm dummies γicontrol for the effect of variables that are constant over time but are excluded fromthe model (e.g. industry classification of firm). Hereby investment is measured aschange in fixed tangible assets plus depreciation; cash flow is the sum of net incomeand depreciation. All variables (except debt and employment growth) arenormalized by the initial size of capital in order to reduce possible

3 See e.g. studies by Fazzari et al. (1988) and Hoshi et al. (1991).4 We also tried to proxy for investment demand with change in output and sales growth, as suggestedby accelerator models of investment. Still the coefficients were almost always statistically insignificant.This result should not be surprising, as other studies that were made by using data from transitioncountries have observed similar results, see e.g. Anderson and Kegels (1997), Prasnikar and Svejnar(1998). Accelerator model of investment means hereby that if the desired capital stock is proportionalto output, then the investment in capital will be proportional to changes in output (see e.g. Bond et al.,1997).

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heteroscedasticity arising from varying size of firms. Capital stock (K) is measuredas the net value of fixed tangible assets. The stock variables are measured at the endof the year; for instance, Kit is the value of capital of firm i at the end of year t.

A standard criticism to interpreting positive cash flow coefficients asevidence of financing constraints is that cash flow might actually proxy for theprofitability of new investment projects. Since Fazzari et al. (1988) the strategy hasbeen to split the sample by some criteria associated with problems of raising fundson the credit and capital markets and compare the relevance of inside firm liquiditybetween different sub-groups. Plausible criteria include inter alia firm size, firmage, the existence of close relationships with industrial or financial groups, thepresence of credit rating or commercial paper programs, dividend policy etc. If forthe class that is a-priori classified as financially constrained, the cash-flowsensitivity is significantly bigger and statistically more significant, then this isinterpreted as evidence of the presence of financing constraints, assuming thatprofits have the same relevance as measure of profitability of new investment fordifferent firms.

We split the sample along two lines. First we use firm size as a proxy for theability to raise funds through external financing. The rationale is that firm size couldbe a proxy for firm age and other unobservable firm attributes that affect the degreeto which public information about the firms’ investment projects is available. Smallfirms probably include many newly created de-novo firms, which lack credit historyand collateral. It is also plausible that the transaction costs of obtaining fundscontain a significant fixed cost component. The presence of such increasing returnssuggests that the cost of obtaining external funds are higher for small than for largefirms.5 It has also been emphasised in earlier studies that in transition economiesthe financing of small and medium sized firms is an important obstacle to growth(Pissarides, 1998). The sample is divided into three equally sized groups (noted as"small", "medium" and "large") according to the average size of real assets over thesample period. Real assets were calculated with GDP deflator.

One possible criticism to the usage of firm size as a criterion of whetherparticular firm is liquidity constrained or not, is that the costs of financing coulddecline with size due to a lower risk for the bank, not necessarily due to smallerinformation problems.6 Smaller firms in particular usually have a lower survivalprobability than large firms (Audretsch et al., 1999) and banks’ loan losses arefound to be much higher for loans made to small firms in comparison to large firms(Churchill and Lewis, 1985). We offer two arguments against this criticism. First,the aggregate risk for banks is smaller in a portfolio consisting of several small loansthan just a few big loans, because in the former case, due to the law of largenumbers, the total return is more stable and the overall risk is smaller. Similarly, in

5 Oliner and Rudebusch (1989) found that transaction costs account for up to 25 % of the grossproceeds of small stock issues and one-seventh of the proceeds of small debt issues.6 We thank one of the anonymous referees for drawing our attention to that issue.


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the insurance industry smaller risks are considered to be more insurable than largeones due to a better spread of claims over time. Secondly, if firms’ owners and bankshad exactly the same information about project risk, then the required rate of returnfrom the risky project is probably higher anyway, so the owners are less willing tofinance these projects. The source of liquidity constraints (or that firms internalfunds and profits are correlated) is the asymmetric information concerning projectsreturns, not just the possibility of the failure of the project.

After investigating the effect of firm size on investment-cash flowsensitivity, we tried to see whether there are any differences in the investmentbehaviour of firms owned by foreign capital versus those belonging to privatedomestic capital. The enterprises of the first group are at least partly subsidiaries offoreign parent companies (as argued by Kangur et al., 1999). So they could receivefunds from the internal capital market of the international corporation, (as firststudied by Hoshi et al., 1991), as well as receive cheaper and longer-term creditsfrom foreign credit markets. We defined firms as belonging to foreign or Estonianprivate capital if in all years of the sample period (1995-98) more than 50 % of theshare capital belonged to foreign owners or Estonian private capital.

In both classifications firms are not allowed to change their groupaffiliation, although in a rapidly developing economy this may be inadequate: smallfirms grow, their net worth increases, and more information on them becomesavailable, so firms’ financial constraint status may change.

Next we report results of estimating equations (4.1) for different samplesplits. As stated, ‘fixed-effects’ or ‘within-groups’ estimators were used. This meansthat the deviations of variables from their firm-means were used in regressions. Asthe regression equation was not derived explicitly from any structural model, theparameters should be interpreted as partial correlation coefficients rather thanestimates of structural coefficients. First, the results for different size groups (seetable 3 below) indicate that the coefficients of both measures of internal liquidity(cash flow and cash stock) decrease monotonically with firm size. The same appliesto the statistical significance of the parameters. This is evidence in favour of thehypothesis that large firms can more easily finance their investments and face lesssevere financing constraints. It is important to emphasize that because cash flowmay actually proxy for the firms' investment demand, it is the difference in theestimated values of parameters that matters rather than just the size of theindividual parameters. The t-statistic under the null hypothesis that small andmedium size firms have the same cash flow coefficient is 2.54. The t-statistic underthe null hypothesis that large and medium sized firms have the same cash flowcoefficient is 2.34. This means that the difference is also statistically significant.Coefficients of leverage variable are negative for small and medium sizedenterprises, but insignificant for large firms. It suggests that strength of balance

17


sheet is more important for smaller firms. Parameters of the employment growthvariable are significant in two out of three regressions, so hopefully we have beenable to control for the existence of investment opportunities at least partially.Adjusted R2-s in investment equations are similar to the ones observed in otherstudies.

Table 3 Effects of employment growth, cash flow, cash stock and leverage

on investments. Estonian manufacturing firms, sample split by firm size,

1996-1998.

Next the results for firms belonging to Estonian vs. foreign capital arepresented. Let us first note that foreign firms tend to be much larger than domesticin terms of average value of assets (see table 2). In order to control for the firm-sizeeffect we split the sample of domestic corporations ordered by the period's averagereal assets into 3 groups (48 firms each): small, medium and large enterprises.Similarly, the sample of foreign corporations was split into two groups (15 firmseach). The foreign firms were divided into two groups due to the much smallernumber of foreign firms in our database. As we can see from table 4, both cash flowand stock have a strong positive effect on investment for different groups ofEstonian firms (except the cash stock for medium sized firms). In comparison todomestic firms, the coefficients are much smaller and less significant for both largeand small foreign corporations. This finding was also robust to other specificationsof the model not reported here (for example, when investment was regressed onlyon cash flow and cash stock etc.). It is interesting that the cash flow parameter forsmall foreign firms is smaller than that of large Estonian firms although the firmsin the second group are about four times larger in terms of total assets (respectively1.09 and 5.13 millions of USD). If only firm size affected cash flow – investmentsrelationship, then the cash-flow parameter would be bigger among large Estonianfirms, not among small foreign firms. The medium Estonian firms are almost ofequal average size (1.05 million USD) to small foreign firms, but the cash flowparameter is about 60 % bigger in that group. We can conclude that affiliation toforeign capital significantly loosens financing constraints, increases investment andthereby supports firm growth. On the other hand the results should be treated with


18

caution since the sample of foreign firms is quite small.Table 4 Effects of employment growth, cash flow, cash stock and leverage

on investments: sample split by both firm size and ownership

(Estonian/foreign owners)

Fazzari and Petersen (1993) argue that estimating equations like (4.1)underestimate the full long-run effect of financing constraints on fixed capitalinvestments since firms smooth investment with working capital to maintaindesired investment levels. So we also estimated the investment regressions thatwere augmented with the working capital investment variable. In order to accountfor the endogeneity of working capital investment, two-stage least squaresestimation was used whereby the working capital investment was instrumentedwith cash flow, employment growth, beginning of period stock of working capital,firm- and year dummies. The results are not reported here due to lack of space(these are available upon request). In general the cash-flow coefficients increasedsignificantly in size but the pattern across size and ownership classes remained thesame. The sign of the working-capital investment variable after inclusion in the leftside of regression (4.1) turned out to be negative. According to Fazzari and Petersen(1993) the last outcome should address the criticism that positive correlationbetween investment and cash flow arises because cash flow proxies for investmentdemand. The intuition is that if it is less costly to decrease working capitalinvestments than fixed investments, liquidity constrained firms should in theperiods of temporary cash flow shortfall decrease rather investments in workingcapital (up to drawing these to negative levels) than in fixed assets that generatesthe negative relationship between the two kinds of investments.

The other possible way to modify the model concerns how far the variationof parameters is tested. Instead of dividing firms into sub-groups and thenestimating the same equation separately for each group one could also use theexpansion method defined by Casetti (1986).7 Let us have the initial model of the

7 We thank the anonymous referee for suggesting the usage of expansion method. Schiantarelli (1996)has also discussed and suggested the usage of interaction terms in the single investment equation whentesting for liquidity constraints instead of grouping firms into sub-samples and then estimating theequation separately for each of them.

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form (4.2) and the expansion equation for parameters of the form

(4.2)

where FOR is the dummy variable indicating whether particular firm belongs to theforeign capital and SIZE is a measure of firm size defined as the natural log of theaverage value of firm’s assets. Then the terminal model becomes

(4.3)

Only the financial variables are expanded here in respect to ownership and size, asit is the variation in these variables that is of interest here. The advantage of model(4.3) is that it saves degrees of freedom, keeps the data together and explains thedifferences due to size and due to ownership in one model. Alternatively, one mayargue that in the first model some variables are omitted, which we expect to be ofimportance (size, type of owners), and hence we would expect biased estimates. Theestimation results are presented hereby in the table 5.

As the reader may see, the qualitative results still hold: both cash flow andcash stock have significant positive effect on investments (as shown by the positivevalues of parameters δ21 and δ31). For the domestic firm with average size (FOR=0and SIZE=log(16 000 EEK)=9.68) 1 kroon increase in cash flow increasesinvestments by 0.522 kroons (i.e. the value of parameter δ21 plus 9.68 times thevalue of δ22). The positive effect of liquidity declines both with firm size (due to thenegative value for parameter δ22) and is smaller for foreign owned firms (negativeδ23). The impact of the leverage or indebtedness variable on investments is stillnegative, but diminishes with the firm size (negative δ41 and positive δ42). Finally itseems not to matter much for the results whether the effect of liquidity is assumedto change with firm size continuously (like here) or discretely (results in tables 3and 4).


20

Table 5 Effects of employment growth, cash flow, cash stock and leverageon investments: the parameters of financial variables expanded with firm

size and ownership

5 Test of liquidity constraints with Euler equationIn order to strengthen the robustness of the above results we used the so-calledEuler equation approach. The Euler equation is a relation between investment insuccessive periods derived from a dynamic maximization problem. Thereby theimpact of expectations and profitability on investments are considered in a moreplausible manner than simply estimating ad-hoc regressions. We used the modelthat has been derived and estimated by Bond and Meghir (1994). The latermodifications of it have been used inter alia by Bond et al. (1997) for Belgium,France, Germany and United Kingdom data and by Lizal (1998) for Czech data.

Bond and Meghir (1994) have derived the model that will be presentedhere. The derivation of the model starts with the assumption that the firm’sobjective is to maximize its net present value. In the absence of taxes it is given atthe start of period t as

(5.1)

In the equation above Πτ( ) stands for firms net revenues or profits, Kt

for capital,Itfor investment, L

tfor variable factors and βt

τ+1 for discount factor between periodst and t+1 that derives from nominal required rate of return r

tas βτ

τ+1 =(1+rt)-1 .

Differently, value of firm can be expressed as the sum of discounted future profits:

(5.2)

The motion of capital stock over time is described with the equation Kt= (1-δ)K

t-1+I

t,

where δ is the rate of economic depreciation. As usual, quadratic linearlyhomogenous function is assumed for adjustment costs (i.e. constant returns to scale

21


with respect to capital and investment):

(5.3)

Parameter b measures the size of adjustment costs; parameter c indicates theoptimal investment/capital ratio based on the adjustment costs. The judgement forthe quadratic form is based on the fact that higher deviations from the equilibriumare more costly than just an oscillation around the optimal level (Lizal, 1998).Differently, due to convex adjustment costs firms should try to divide the desiredinvestment over several consecutive periods instead of making all the investment inone period. Firm’s net revenues Πτ will be then as follows:

(5.4)

The expression F(Kt,Lt) is the constant returns to scale production function,wtis the vector of prices for the variable inputs Lt and pt

I denotes the price ofinvestment goods. In order to allow for imperfect competition, pt is let to depend onoutput, with constant price elasticity of demand (ε >1). The optimal investment pathcan be described in terms of an Euler equation that under the aforementionedassumptions derives as follows (the complete derivation can be found at Bond andMeghir 1994):

(5.5)

where Jit denotes user cost of capital. So, in the Euler equation (that relatesmarginal adjustment costs in adjacent periods), current investment is positivelyrelated to expected investment and to the current average profits (reflecting themarginal profitability of capital under constant returns), and negatively related tothe user cost of capital.8 An attractive feature of the Euler equation model is that allrelevant expectational influences are captured by one-step-ahead investmentforecast (Bond et al., 1997). So, it should control for the usual criticism to othertypes of models, that financial variables do not capture the effect of liquidityconstraints, but rather expectations of future profitability.

When expectations are replaced with realizations and parameters in theresulting regression equation are assumed to be constant over time, then theempirical specification of the Euler equation will be as follows:

8 Jorgenson (1965) introduced the notion of the user cost of capital. Absent taxes, the user cost iscalculated as follows. First, the sum of opportunity cost of funds and depreciation minus expectedappreciation of capital goods is calculated. Then the result is multiplied with the relative price of capitalgoods. See also equation (5.7).


22

(5.6)

where β1=c(1-φt+1), β0=(1+c)φt+1, β2=-φt+1, β3=-φt+1/b(ε-1), β4=-φt+1/bα, β5=-β4 ,φt+1=(1+r t+1)(pt/pt+1)/(1-δ) and α=1-(1/ε) > 0. The term Cit = ptYit - wtLit is cash flow andthe term Yit =Fit - Gitis output net of adjustment costs. The regression variables arecalculated from empirical data as follows. First, like previously investment (Iit) ismeasured as a change in fixed tangible assets plus depreciation. Output Yit is thesum of sales revenues plus change in finished goods inventories. Cash flow Cit ishere defined as operating profits before taxes and interest plus depreciation of fixedassets9 . Finally, the user cost of capital Jit is derived from the model as:

(5.7)

When we calculated the user cost of capital, two-digit producer price indexwas used for pt and the deflator of gross capital formation expenditures for pt

I. TheStatistical Office of Estonia publishes the data on both price indices. Required rateof returnrt was proxied with long-term interest rate on bank loans nominated inEstonian kroons in the beginning of the period. The Bank of Estonia publishes thedata about the interest rates. Notice that quite often the user cost of capital term iseliminated from the model altogether and replaced with fixed time and firm effects(see e.g. Bond, Meghir, 1994). Following Whited (1992) the rate of economicdepreciationδi is calculated as δ= 2/L, where L denotes the estimated average life ofcapital goods, and Lt is calculated as Lt=(GKt-1+It)/DEPRt, where GKt-1 is the reportedvalue of gross fixed tangible assets and DEPRt is reported depreciation.

In the equation (5.13) the term (Yit/Kit-1) is non-zero in the case of imperfectcompetition or non-constant returns to scale. Lagged investment terms considerthe effect of adjustment costs. By estimating the equation we are controlling for therelation between current profits and expected future profitability (Gaston andGelos, 1999). Under the null hypothesis of no financial constraints, the parametersshould satisfy conditions β1 ≥1, β2 ≤1 and β4<0. Under the alternative, the equationis misspecified and then one would expect a positive sign for the coefficient of theprofit in the equation due to liquidity constraints.

Consistent estimates for the parameters of the described dynamic paneldata model can be obtained with an appropriate method of moments (Bond andMehgir, 1994). Ordinary least squares estimates of dynamic panel data models maylead to over- or underestimation of autoregressive coefficients (Bond et al., 1997).The estimator of Arellano and Bond is used here (Arellano and Bond, 1991).9 Here the cash flow is calculated before taxes as the model disregarded taxes. Actually one may arguethat for firms the relevant figure is rather the cash flow available after corporate income taxes. Section6 discusses briefly the impact of changes in taxation on liquidity constraints.

23


Instruments used are lagged one or more periods, because efficient GMMestimators will typically exploit a different number of instruments in each timeperiod. (Doornik et al., 1999) The estimator proposed by Arellano and Bond forestimating the linear dynamic panel data models (as the one here) is a two-stepestimator. In the first step, some known matrix is used as a weighting matrix. Here,it means using an identity matrix (as variables are not transformed, for example,into levels or orthogonal deviations). In the second step, the residuals of the firststep will be used to produce an optimal new weighting matrix. Both estimates areconsistent, but the two-step estimator is more efficient if the residuals from the firststep are heteroscedastic. On the other hand, in small samples the estimatedstandard errors of the second-step estimates are usually biased downwards, so (asusual) results of both first- and second step estimation are reported. Finally, theSargan test of overidentifying restrictions is reported to check for the validity ofinstruments, as well as tests of serial correlation in residuals. The model wasestimated with DPD (Dynamic Panel Data) program written in Ox programminglanguage by Doornik, Bond and Arellano (Doornik et al., 1999). In order to measurethe different effect of liquidity constraints on investment through different sizeclasses of firms, interaction dummies are used for small, medium and large firms(denoted as Dsmall,Dmedium,Dlarge) as well as domestically incorporated vs. foreignfirms ( Dest,Dfor"est" is for "Estonian" and "for" for "foreign").

Results in table 6 indicate that cash flow affects investment positively, sothe null hypothesis of the absence of liquidity constraints is rejected. Also, asexpected, internal funds are more important for investments among small andmedium sized firms. So smaller firms have more liquidity constraints than the bigones, but as the sign of cash flow is positive even for the latter, they may also havesome problems in raising funds. The parameters of the lagged investment terms arecorrectly signed, but smaller in absolute value than suggested by the model in theabsence of liquidity constraints. Output term is non-zero, which can be due to eitherimperfect competition or nonconstant returns to scale. Also the parameter of theuser cost of capital has the expected sign and its two-step estimate is significant at10 per cent. Sargan test (the test of overidentifying restrictions) shows that theinstruments are not correlated with the residuals, which confirms the validity of thechosen instruments. This is also shown by the lack of autocorrelation in residuals.The total number of moment restrictions generated by instruments was 43 (7 for thefirst, 14 for the second and 21 for the last period; the one extra instrument is theconstant term).


24

Table 6 Results of estimating Euler equation: effects of firm size on cash-

flow investment relationship

Table 7 in columns (i) and (ii) presents the results of the estimation of equation(5.6) when firm ownership dummies are interacted with cash flow variables. As wecan see, cash flow coefficient is significant for firms owned by Estonian capital butnot for the group owned by foreign capital. This is in accordance with the previousresults from reduced-form equations. The coefficients for the other right-hand–sidevariables are correctly signed and their statistical significance has increased incomparison with the previous table. In order to control for the different average sizeof Estonian and foreign firms the two sub-samples were further divided accordingto size. That was done with interaction dummies Dsmall_est, Dmedium_est, Dlarge_est,Dsmall_for, Dlarge_for. So we have five groups of firms as in table 4 of section 4. As wecan see from table 7, the cash-flow sensitivity of Estonian firms decreases with size,but that of foreign firms actually increases (coefficient is negative for small, butpositive for large firms)!

The latter finding is not consistent with the hypothesis about presence ofliquidity constraints. The finding of a larger cash flow parameter in the group oflarge firms is sometimes interpreted as an evidence of over-investment among largefirms due to managers’ incentives to cause firms to grow beyond optimal size (Vogt,1994). On the other hand the over-investment hypothesis would rather apply tofirms with dispersed than concentrated ownership structure, because then theshareholders’ control over managers actions is weaker (Schaller, 1993). Althoughwe do not have more detailed information regarding firms’ ownership structure thatis in general not the case of foreign owned firms in Estonia. Another argument couldbe that the over-investment may occur not only when ownership is dispersed butalso when it is remote, and this is the case for foreign firms in Estonia.10 Still, as

10 We thank the editor for suggesting this argument in favour of the over-investment hypothesis.

25


most foreign investments have come into Estonia from geographically closecountries such as Sweden and Finland, it may not be too hard for foreign owners toconstantly control the actions of local managers. Also, the scatter plot ofinvestment-capital ratio versus firm size (log of assets) seemed to provide noevidence whatsoever for over-investment by large foreign firms. Thecounterintuitive results here could be due to the small sample size – there are only15 firms in the group of large foreign owned firms that is well below the usual sizeof firm group in similar studies. It is important to mention that the various simplerregressions in this paper did not show significant effects of cash on large foreignfirms’ investments. Thus we could rather remain with the conclusion that affiliationto foreign owners is associated with better possibilities to finance profitableinvestments than see liquidity constraints affecting foreign owned firms.

Table 7 Results of estimating Euler equation: effects of firm size and

ownership on cash-flow investment relationship

6 Conclusions and implicationsThis paper discussed the relationship between financing conditions andinvestments in Estonian manufacturing firms. We argued that financing constraintsshould be quite material determinants of investment levels for many firms, inparticularly small firms and domestically incorporated firms (as compared to firmsowned by foreign capital). We found from both simple OLS regressions and themore elaborate Euler equation that small (and Estonian) firms are more dependenton their own cash flow and cash stock than larger (and non-Estonian) firms. We


26

interpreted these results to be the evidence of the presence of the liquidityconstraints because inside firm funds influenced investments significantly moreexactly in those enterprises that we assumed to be more financially constrained. Asecond argument in favour of our interpretation is that cash flow affectedinvestments even when investment opportunities (or the profitability ofinvestments) were controlled.

This study confirms the claims of Raudsepp and Leoshko (1998) thatfinancial problems are more acute for small and medium-sized enterprises inEstonia. Secondly, most previous studies of liquidity constraints have not includedthe lower tail of the size distribution of firms (i.e. firms whose size is well below theaverage firm size) in the sample because they were made with databases on firmslisted in stock exchanges. For example, in a thorough study of cash flow-investmentrelationship and firm size made by Kadapakkam et al. (1998) small firms hadaverage assets of $ 57 million in the United States, £ 36 million in Great Britain andDM 355 million in Germany. So the results here may have importance forinvestment literature in general because they show that there is a negativerelationship between investment-cash flow sensitivity and firm size amongenterprises with assets ranging from less then 0.5 million US dollars to themaximum of about 50 million USD.

Another issue concerns government subsidies and whether they should bemore actively used to solve the financing problems of small and medium sizedenterprises by reducing the cost of capital and improving access to long-termsources of external funding. In particular, Raudsepp et al. (2000) are concernedabout the lack of subsidizing of small firms in Estonia resulting in too high cost ofcapital exceeding the internal rate of return of investment projects. So the results ofthis study can be viewed to confirm these claims. This study showed that small firmsmight indeed have problems in financing their investments, however it is not surewhether subsidies would solve the problem. For instance, Demirguc andMaksimovic (1996), for the thirty-country sample, found no evidence thatgovernment subsidies were associated with the ability of firms to grow faster thanwhen relying only on the their internal resources. Rather one can see here a role forventure capital funds targeting at providing finance for growing small and medium-sized businesses – as we saw, small and medium sized firms receive substantiallyless new equity capital.

One other possible implication of the results is related to the recent changesin taxation of corporate income. In Estonia retained earnings are exempt fromtaxation since 2000. Thereafter, corporate income tax in Estonia can be viewed likethe "cash flow tax" suggested by Fazzari et al. (1988b). The notion means that onlythe share of profits that exceed investments are taxed. On the assumption that firms

27


follow the financing hierarchy (investments are first made from retained earningstill these are exhausted) and pay dividends only when they do not have profitableinvestment projects, the new Estonian corporate income tax is similar to a "cashflow tax". This kind of change in taxation lowers average tax rates for growing firmsthat invest all of their internal finance and thereby increases their capitalexpenditures. On the other hand, for mature firms with internal cash flow excess ofinvestments marginal incentives to invest are preserved, so the overall level ofinvestments will increase. Based on a model from the Tobins q theory, Funke (2001)predicted that capital stock would increase 6.1% in the long run due to the year2000 income tax law. According to the previous arguments financing constraintsmay amplify this positive effect.

The other main finding of the analysis was that cash flow is not animportant determinant of investment for foreign owned firms. Indeed, this is not anew result for transition economies; Lizal and Svejnar (2000) reached the sameconclusion when studying the investments of Czech enterprises. However, thisfinding might well serve for further discussion and interpretation. One way in whichliquidity constraints could be relaxed is through the development of the bankingsector: if banks become more capable of monitoring loan applicants then theasymmetric information problems will be reduced and profitable investments aremore likely to receive outside funds. But the development of banking may take quitesome time. On the other hand, the inside flow of foreign direct investment may relaxbinding liquidity constraints much faster by enabling firms to obtain funds fromforeign debt and capital markets.

References

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Funke, M., 2001. Taxation and investment: the impact of the year 2000 incometax law in Estonia. Unpublished paper, 12 pp.

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31

Gender Wage Differences in Soviet and Transitional Estonia

Gender Wage Differences in Soviet andTransitional Estonia1

Charles Kroncke2 and Kenneth Smith3

AbstractWe use the retrospective, covering the years 1989 - 1994, Estonian Labor ForceSurvey to examine potential wage discrimination against women. We look at full-time workers of Estonian and Russian ethnicity in the years 1989, the Soviet period,and 1994, the last full year of the survey and three years after Estonianindependence and the beginning of the transition to a market economy. We findsubstantial evidence of wage discrimination against women in both years. In fact,despite the official rhetoric of gender equality in the Soviet Union, our resultsindicate the relative level of wage discrimination against female workers in Estoniachanged very little between 1989 and 1994 when occupational dummies areexcluded from the wage equations.

Keywords: Gender, wage discrimination, wage decompositionJEL-Code: J71, P23

I. IntroductionIn the former Soviet Union gender related wage and employment differences werenot widely analyzed due to the official declaration of equality between men andwomen. The breakup of the Soviet Union has opened this topic up to discussion andanalysis. In 1995, a detailed retrospective labor force survey was released inEstonia. The survey covers the 1989-1994 period. Thus it contains empirical datarepresenting both the end of the Soviet period and the beginning of the transitionalperiod to a market economy.

We use the Estonian Labor Force Survey (ELFS) to measure potential wagediscrimination against women. The years 1989 and 1994 are analyzed as 1989represents the Soviet period (to our knowledge, detailed analyses of gender wagedifferentials in the Soviet Union do not exist), and 1994 represents the last full year

1 We would like to the thank James Long, Dorothe Bonjour, Alf Vanags, Bruce Smith, Jens Larsen,participants of the European Association of Labour Economists 1998 meeting in Blankenberge,Belgium, and an anonymous referee for useful comments on earlier drafts. Dmitri Kulikov providedexcellent research assistance. Any remaining errors are the sole responsibility of the authors.2 Gordon College. E-mail: [email protected] Millersville University. E-mail: [email protected]


32

of the survey and a year two years past the start of substantive economic reform inEstonia.4 The data provide substantial evidence of significant wage differentialsbetween men and women in both 1989 and 1994 that cannot be accounted for byproductivity relevant characteristics available in the data set. The relative level ofthe wage differentials explained by the data are quite close in 1989 and 1994 whenoccupational dummies are not used in the wage regressions. This would implyapproximately the same amount of wage discrimination against women in theSoviet period and the more recent transitional period. When the wage regressionsinclude occupational dummies, a significantly greater portion of the wagedifferential is explained in 1989 than in 1994. Throughout this study we focus onfull-time workers.

A caveat is in order when examining earnings inequality in the Soviet andtransition periods. It is likely that inequality in material standard of living is closelyrelated to income inequality in a market or transition economy. However, in theSoviet Union, due to goods shortages and the provision of services by the state, it islikely that material well-being was more evenly distributed than was income.

This paper has five sections. Section II presents a brief literature review andan overview of models of wage discrimination. Section III gives a more detaileddescription of our data and methodology. Empirical results are presented in SectionIV, and Section V presents concluding remarks.

II. A Model of Wage DiscriminationOur method of estimating gender wage discrimination in Estonia is based on thestandard Becker (1971) model that was formalized for empirical testing by Oaxaca(1973). We assume that, in the absence of discrimination, an individual’s actualwage (W) is equal to his or her marginal productivity (W*). In the absence ofdiscrimination the following equality should hold:

(1)

Where bar notation indicates mean values (the M subscript denotes maleand the F subscript denotes female). If this equality does not hold, we take it asevidence of gender-based wage discrimination. The larger the deviation fromequality, the greater the evidence of wage discrimination. Oaxaca used this modelto measure gender-based wage discrimination in the U.S. using wagedecomposition methodology. The first step in the wage decomposition procedure isto use OLS to estimate the returns to certain individual characteristics (education,experience, etc.) and other work-related factors (industry, region, etc.) that affect

4 Recently, the results of the 1997 ELFS were released. This data covers 1995-1997. However, thisdata does not use the same sample of individuals as the 1995 ELFS. Thus, a comparison of 1997 (or1996 or 1995) and 1989 would be more problematic than a 1989-1994 comparison.

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productivity. Once these estimates are obtained they may be used to decompose themale-female wage differential into two components; one component is the result ofdifferences in productivity relevant characteristics, and the second unexplainedcomponent is generally attributed to discrimination.

The following is a brief overview of wage decompositions. If βM and βFrepresent the OLS coefficient estimates for vectors of personal characteristics andwork-related factors XM and XF, then the mean preserving nature of OLS regressionimplies that:

(2)

If there is no wage discrimination in the labor market, then we should be able tomake the following substitutions:

(3)

Equations (2) and (3) imply that we should be able to substitute the returnsto individual characteristics for males and females without affecting wages if nodiscrimination is present. In other words, in the absence of discrimination,differences in personal characteristics and job related factors (that may themselvesbe a result of discrimination) should cause wage differentials not differences in thereturns to those characteristics and factors.

Employing algebraic manipulation and taking the natural logs ofindividuals’ wages, Oaxaca decomposed the natural log wage (ln(wage)) differentialas follows:

(4)

(5)

(6)

The unexplained portion of the wage differential (equation (6)) is then attributed topotential discrimination. Using natural logs implies in equations (4) - (6) andthroughout the paper that:

(7)

where n is sample size. The way the decomposition is presented above implies thateliminating discrimination would tend to pull up female wages toward the malelevel. Conversely, one could perform an analogous decomposition applying the


34

female coefficient estimates to male characteristics. This would imply thateliminating discrimination would tend to pull male wages down toward the femalelevel. Oaxaca recognized this "index problem". Since the two decompositions yieldvery different results, the standard practice is to do both decompositions andrecognize that the actual level of discrimination is somewhere between the twoindicated levels5.

The imprecision of this method is certainly unsatisfactory. Thedecomposition methodology has been refined by Cotton (1988). Cotton’s primary,and quite logical, criticism of the standard decomposition method, was that, werediscrimination suddenly eliminated, we should expect the wages of the majoritygroup to fall and those of the minority group to increase. This fact was implicitlyaccepted by Oaxaca - hence the two decompositions. Cotton thus reasoned that thereturns to personal characteristics and other factors would, in the absence ofdiscrimination, be a weighted average of the two groups with the weights beingequal to the respective groups’ proportion of the population or sample within thedata set. In our case this implies using the following in our decompositions:

(8)

where α represents the weight placed on the male coefficients.

Employing Cotton’s method, we can decompose the total ln(wage) differential,ln(WM) - ln(WF), as follows:

(9)

(10)

(11)

Equation (9) can be rewritten as:

(12)

Equation (12), in turn, implies:

(13)

5 The decomposition shown tends to attribute more of the wage differential to discrimination thanwould applying the female coefficient estimates to male personal characteristics. For a detailedexplanation of this phenomenon, see Cotton (1988).

35


Thus, the decomposition methodology yields a direct means of comparing the ratiospresented in equation (1).

The implication of this decomposition is that discrimination is manifestedin two ways: the first through the overvaluation of the male group’s characteristicsand the second through the undervaluation of the female group’s characteristics.Cotton’s methodology is used in this paper and recently has been used to studyethnic wage discrimination in the Netherlands (Kee (1995)).

This study is not the first analysis of male-female wage differentials inEstonia. Kandolin (1996), using 1993 survey data, found that women’s wages were82% of men’s wages in her sample.6 In this study, the author estimates that ifEstonian women were to receive the same returns as men for their education, jobtenure, and family responsibilities, their wages would be 108% of men’s wages.

Using the more recent, more detailed, and much larger ELFS, our findingsfor adjusted wages in 1994 are rather similar to Kandolin’s 1993 result. Asmentioned, the ELFS also allows for comparisons across economically divergentyears, and allows us to compare how women fared relative to men in 1989 as well asin the early transition. The ELFS data indicate that women’s relative wage position(in financial terms and in terms of the part of the financial differential that can beexplained by the data) in a transitional economy might not have changed by asmuch as many would think.

Further our results indicate that gender inequality in the Soviet Union mayhave changed relatively little in the wake of Gorbachev’s economic reforms. Thelevels of gender inequality we find in 1989 are consistent with what little evidenceexists for earlier Soviet periods. For example, Vinokur and Ofer (1987) find evidenceof substantial gender earnings inequality in the mid-late 1970s.

III. Methodology and DataHere, the wage decomposition used is as demonstrated in equations (8) - (11) whereβ* is a weighted average of the OLS coefficient estimates for males and females. Theweights used are the groups’ relative proportions in the data samples (as in Kee).Four wage decompositions are constructed: two each for 1989 and 1994. In eachyear, one decomposition is constructed without occupational dummies and one isconstructed using occupational dummies.

The data are from the retrospective 1995 ELFS. In all, 9608 people betweenthe ages of 15 and 74 were interviewed. The interviewees were selected at randomand distributed throughout Estonia. The results of the interviews were compiled by

6 Only ethnic Estonians are included in the Kandolin study.


36

the Commission for Population and Social Statistics Working Group for LaborForce Surveys. The Working Group was initially formed by the Estonian LaborMinistry. The interviews took place in early 1995. Interviewees were askedquestions concerning their work history (that is their entire employment record)during the 1989 - 1994 period. The retrospective nature of the survey also makes thechoice of comparing 1989 and 1994 logical. Unemployment in 1989 was essentiallynonexistent in Estonia and the stability of the ruble and of incomes in general makethe wage data relatively reliable. For the 1994 portion of the survey, the questionspertained to a period only a few months prior to the interviews.

While, in general, the retrospective nature of the survey may tend to castdoubt on data reliability, this survey represents the only detailed labor dataavailable for Estonia during the late Soviet - early transition period. Further, theretrospective nature of the survey does largely eliminate the possibility of selectionbias when comparing 1989 and 1994. We do not need to worry about the possibilityof losing people to emigration, death, or failure to report between our 1989 and1994 samples as all respondents were interviewed in early 1995 about their workhistories. Thus, most of the people in our 1989 sample are also present in our 1994sample. There are normal changes due to the retirement of older workers and youngpeople entering the labor force between 1989 and 1994. There was also a significantdecline in labor force participation rates between 1989 and 1994. This decline wasgreater for women than for men (Eamets et al. (1997)). This change in labor forceparticipation is also reflected in our samples.

Table 2 presents mean values of characteristics for the two groups in both1989 and 1994. Definitions of the variables are in Table 1. Wages and natural logsof wages for 1989 are in Soviet rubles (RB). The ruble was stable throughout 1989at an approximate value of 1 RB = 1.67 USD (this was the official, if artificial,exchange rate (Buckley and Ghauri (1994)). Wages and natural logs of wages for1994 are in Estonian Kroons (EEK). In December 1994, the EEK/USD exchangerate was 12.57 (Estonian Institute of Economic Research (1997)). The EEK waspegged to the German Mark at a rate of 8 EEK = 1 DEM. The mean values for thedummy variables can be interpreted as percentages. For example, the values forENGLISH in 1989 imply that 17.82 percent of males and 21.64 percent of femalesin our respective samples indicated an ability to speak English at that time. In ourregressions, ln(WAGE) is the dependent variable. All other variables in Tables 1 and2 are used as regressors except WAGE.

In many respects, our choice of independent variables is comparable toother studies of wage discrimination (Oaxaca, Cotton, Kandolin, and Kee all serveas examples). Naturally, the unique circumstances of Estonia and the limitations ofthe data make our choice of regressors somewhat unique.

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English language ability is used as English now serves, and has served, asthe primary language of business between Estonia and its primary new tradepartners in the Nordic countries, the EU, and the U.S. English language abilitytends to be in high demand in Estonia though it is far from a universally spokenlanguage. ETHNIC is included to account for wage differences that may be theresult of differences between Estonians and ethnic Russians7. TALLINN andNARVA are our two choices for regional dummies. Tallinn is the capital and thelargest city of Estonia. In fact, Tallinn is the only "large" city in Estonia with apopulation of about 450,000. The next largest city, Tartu, has less than one-fourththe population of Tallinn. Narva, in northeast Estonia, was where Estonia’s heavyindustry was concentrated during the Soviet period. This heavy industry is nowlargely idle and has been since the breakup of the Soviet Union. The Narva regionwas in 1994, and still remains, a depressed region in Estonia.

The other variables in Tables 1 and 2 are standard measures of returns toexperience and job tenure and a series of industry dummies. There is a debate as towhether occupational dummies should be used in wage discrimination regressionsas wage differentials may be the result of discrimination against women (or racialor other minorities) wishing to enter certain high-paying occupations. Dolton andKidd (1994) found little evidence of occupational segregation amongst the sexesleadng to unexplained wage differentials in the United Kingdom. However, Gill(1994) found evidence that differential access to high-paying occupations played asignificant role in racial wage differentials in the U.S. The implication here is thatthere may be (or may have been in Soviet times) barriers in place preventing womenfrom entering some high-paying occupations in Estonia. If so, women mightbecome concentrated in low-wage occupations. Thus the inclusion of occupationaldummies could be inappropriate leading to a severe underestimation of actual wagediscrimination.

Bearing this in mind, we do examine male-female wage differentialsincluding occupational dummies. Including occupation has a significant effect onthe explained differential in 1989 but very little effect in 1994. As is discussed below,it is quite plausible that the lack of occupational effects in 1994 is due to labormarket restructuring that occurred during the early transition period. Table 3presents employment by occupational category.

IV. Empirical ResultsSince empirical estimations of wage discrimination are derived from profitmaximization models where the wage is equal to the marginal product of labor, it islegitimate to ask whether this type of model is appropriate for looking at wage

7 Only ethnic Estonians and Russians are included in this study. Ukrainians, Belarussians, non-Estonian Balts, Finns, etc., make up a total of about seven percent of the Estonian population. (UNDP(1996)).


38

differentials in the former Soviet Union or any other planned economy. While it isunlikely that profit maximization is a good hypothesis for Soviet enterprises, theempirical estimations are truly only estimating returns to certain factors relevant toone’s work. Thus, one would assume, that in a socialist economy where equality wasin principle a key objective of the state, it should also hold true that individuals arecompensated equally for like characteristics. In fact, one should expect that themain difference between wages in a socialist economy and a market economy wouldbe that wage differentials for disparate characteristics are minimal in a socialisteconomy. Thus, while the initial discussion of wages and marginal productivity maynot be relevant for Soviet Estonia, we still believe the empirical wage decompositionmodel is a legitimate and interesting means of exploring wage differentials in asocialist economy. The results bear this belief out.

Table 4 presents the OLS regression results for 1989 when occupation isexcluded, and Table 5 presents the OLS regression results when occupation isincluded.

To perform the wage decomposition for 1989, we substitute our regressioncoefficients into equations (9) - (11). β* is computed as in equation (8) with α =0.492. This yields the following:

Occupation NOT included

(14)

(15)

(16)

The observed female-male wage ratio and the adjusted (productivity) female-malewage ratio from equation (14), respectively, are:

(17)

Occupation included

(18)

(19)

(20)

39


Equation (18) then yields the following adjusted wage ratio:

(21)

The mean ln(wage) differential in the 1989 samples (male-female) is 0.4221(without occupation) and .4220 (with occupation - this difference reflects minorrounding errors when using the coefficient estimates and mean values to performthe decompositions).

Table 8 presents a specific breakdown of the 1989 ln(wage) differential byindividual characteristics, region, and industry. As equation (17) indicates, slightlyless than 16 percent of the female-male wage differential can be explained by thedata when occupation is excluded. While women made 34.5 percent less than mendid in 1989, their estimated productivity was only 5.4 percent less. Whenoccupational dummies are included (see Table 9 for the decomposition results) inthe wage equations, almost 38 percent of the female-male wage differential isexplained by the data. Thus, even with occupation, there is still substantial evidenceof wage discrimination against women in Soviet Estonia. In both decompositions,the unexplained wage differential is, roughly, split evenly between anundervaluation of female characteristics and an overvaluation of malecharacteristics.

Table 6 presents the regression results for 1994 with occupation excluded,and Table 7 presents the regression results including occupational dummies.

Again, for 1994, we substitute our regression coefficients into equations (9)- (11) with β* computed as in equation (8) (α = 0.508). This yields the followingresults:

Occupation NOT included

(22)

(23)

(24)

In 1994, the observed and adjusted wage ratios are:

(25)


40

Occupation included

(26)

(27)

(28)

With occupational dummies included, the adjusted wage ratio becomes:

(29)

The mean ln(wage) differential in 1994 is 0.3094 (without occupation) and 0.3085(with occupation).

In this case, with occupation excluded, slightly more than the entire actualln(wage) differential is unexplained implying women should have earned more thanmen given the characteristics we have examined and the data we have. Again theunexplained differential is almost evenly split between an overvaluation of malecharacteristics and an undervaluation of female characteristics, and again Table 8presents a specific breakdown of explained wage differentials by variable. Whenoccupation is included (see Table 9), equation (29) indicates women are slightly lessproductive than men. However, the inclusion of occupation has remarkably littleeffect in 1994. With or without occupation included, the data indicate men andwomen should have had virtually identical labor earnings even though, in reality,women only earned about 69 percent of what men did in our samples.

Table 8 shows that, taken together, the industry dummies are the mostimportant factor explaining the 1989 male-female wage differential in the absenceof occupational effects. Personal characteristics, as might be expected, played arelatively small role during the Soviet period. However, they along with regionfavored higher female wages overall. However, differentials due to the sector oneworked in were quite important determinants of individual wages (as indicated byTable 4) as well as male-female differentials. Two sectors, agriculture and fishing(AGFISH) and mining, manufacturing, electricity, and construction (MMEC), paidboth men and women particularly well in 1989. As Table 2 indicates, these twosectors employed over 70 percent of all male full-time workers. However, the samesectors employed only 46.5 percent of female full-time workers. Conversely, theeducational (EDUC) and health care (HEALTH) sectors were two of the poorerpaying areas even in 1989. While these two sectors accounted for only 4.1 percentof male full-time employment, 19.4 percent of women working full-time wereemployed in EDUC or HEALTH. Clearly there was a high degree of industrial

41


segregation by gender in Soviet Estonia with men dominating employment in thehigh-paying sectors and women dominating employment in the low-paying sectors.If this fact itself is a result of gender discrimination, it is possible our results inequations (14) - (16) significantly underestimate overall gender wage discriminationin 1989.

Table 9 illustrates the importance of occupational effects in 1989.Occupational effects dominated the explained 1989 wage differential and, togetherwith sectoral effects, accounted for slightly more than the entire explaineddifferential. Two occupational categories had positive regression coefficients forboth men and women in 1989: management and skilled labor. As Table 3 indicates,these two occupational categories accounted for about 78.6 percent of maleemployment. However, the same two occupations employed only around 32.5percent of women. Conversely, women dominated employment in low-wageoccupations. Four occupational categories had negative OLS coefficients for bothgenders - technical, clerical, service, and unskilled labor. These occupationsemployed over 47.5 percent of all women working full-time in 1989 but just 9.1percent of men. While the data do not allow for testing of occupational segregationin 1989 (at least not by conventional methodology employed by Gill and Dolton-Kidd), it is difficult to believe that occupational segregation in Soviet Estonia wasnot due, at least in part, to discrimination. It is likely that this was also true ofsectoral segregation.

Personal characteristics were far more significant in 1994, and the overallsignificance of sector diminished somewhat (see Table 8). Table 6 showsemployment in AGFISH continued to significantly influence wages for both menand women in 1994. However, in 1994 working in the AGFISH sector tended todepress wages - hurting far more men than women. Men continued to dominateemployment in the still fairly lucrative MMEC sector. Women were still hurt bytheir relatively high concentration in the low-paying health care and educationsectors. As in 1989, differences in personal characteristics tended to favor women -particularly education (EDU) and English language ability. Women on average hadapproximately one-half more year of education than men. Further, as market forces(marginal productivity) tended to drive wages more, the returns to educationincreased sharply8. The fact that women were considerably more likely to speakEnglish and work in Tallinn also had significant positive effects on their wages. In1994, these personal characteristics dominated sectoral wage effects with the endresult that, according to our estimations, women should have earned slightly morethan men.

8 Increasing returns to education during the economic transition to a market economy have been foundelsewhere in Central and Eastern Europe. For a detailed example, see Rutkowski (1996).


42

Table 7 presents the effects of individual variables on wages in 1994 withoccupational dummies included in the wage regressions. The net effect ofoccupation is extremely small and the overall explained wage differential changesonly slightly with the inclusion of occupation. In fact the direction of the overalloccupational effect is unique to our knowledge. As mentioned, many economistsargue that including occupation is problematic as it may lead to an underestimationof wage discrimination against women (or other minority groups) who likely facediscrimination when trying to enter high-wage occupations. However, intransitional Estonia, the net effect of occupational dummies actually favors women- if only slightly. While the high level of occupational segregation may well havebeen the result of discrimination against women in Soviet Estonia, the nature of theeconomic transition seems to have worked against men as far as occupation isconcerned. In 1994, three occupational categories had positive coefficients for bothsexes - management, professional, and technical. It is not surprising in a marketeconomy that the three "white-collar" occupations requiring specialized trainingwould receive wage premiums. While men were considerably more likely to be inmanagerial positions in 1994 (15.04 percent of men as opposed to 9.57 percent ofwomen held such positions), women dominated employment in professional andtechnical positions (35.17 percent of female full-time employment as opposed to14.86 percent of male full-time employment). Not surprisingly, managerialpositions paid quite well in 1989 while professional and technical positionsgenerally paid men (as well as women) more poorly than skilled labor. Women werehurt by the fact that they also dominated the one occupational category that had alarge and significant negative impact on wages for both men and women - unskilledlabor. Just over eight percent of women worked as unskilled laborers in 1994 whileless than four percent of men were in unskilled labor positions.

V. ConclusionsDespite official Soviet proclamation, our analysis indicates a large gender wagedifferential in Estonia at the end of the Soviet period - in fact larger in relative termsthan the 1994 wage gap. Little of this differential can be explained by differences inpersonal characteristics and other job related factors present in the ELFS. Evenincluding occupational dummies leaves the majority of the differential unexplained.This large unexplained differential remained throughout the early transitionalperiod. Though, in fact, in relative terms, the unexplained male-female wagedifferential grew. However, the relative growth in the unexplained portion of thedifferential might be less than many would expect.

Potential gender wage discrimination (as well as other types of potentialdiscrimination) provides a particularly acute policy problem for transitionaleconomies in general and for Estonia specifically. While established marketeconomies have wrestled with the problem of wage discrimination for years and

43


erected elaborate (if not always effective) legal means of dealing with discriminationproblems, discrimination was ignored (as it officially did not exist) in socialisteconomies. As Estonia gets deeper into negotiations for EU membership, it will beforced to deal with the question of discrimination. As of yet, Estonian policy makersand legislators have not addressed the issue in any substantial way.

As mentioned above, the job segregation that occurred in Soviet Estoniamight actually be aiding women during the transition. Women in Soviet Estoniawere relatively concentrated in jobs requiring specialized training despite the factthey did not pay particularly well. While the market has pushed up the wages forthese occupations (particularly professional and technical occupations) individualshad not, as of 1994, had time to adjust to these market forces. It is plausible thatmore men will be attracted to now relatively high-paying professional and technicalpositions. It is also plausible that women may be crowded out of these occupations.It is quite possible the relative position of women in Estonia (and perhapstransitional economies in general) will get worse before it improves. Examination ofthe 1997 ELFS and subsequent labor force surveys may indicate how the problem ofgender wage discrimination is addressing itself.

Table 1: Variable Definitions


44

Table 2: Mean Characteristics

Table 3: Employment by occupation (percentages)

45


Table 4: Regression Results, 1989 - Occupation not included

dependent variable: ln(wage)

Table 5: Regression Results, 1989 - occupation included



46

Table 6: Regression Results, 1994 - occupation not included


Table 7: Regression Results, 1994 - occupation included


47


Table 8: The wage effects of variables (Occupation not included)

Table 9: The wage effects of variables

Occupation included


48

References

Becker, Gary S. (1971), The Economics of Discrimination, 2nd edition. Chicago:University of Chicago Press.

Buckley, Peter J. and Ghauri, Pervez N. (1994), "Statement of the Issues." TheEconomics of Change in East and Central Europe: Its Impact on InternationalBusiness. Academic Press,: 1-32.

Commission for Population and Social Statistics Working Group for Labor ForceSurveys, (1995) Estonian Labor Force Survey 1989-1995.

Cotton, Jeremiah (1988), "On the Decomposition of Wage Differentials." TheReview of Economics and Statistics 70: 236-43.

Dolton, Peter J. and Kidd, Michael P. (1994), "Occupational Access and WageDiscrimination." Oxford Bulletin of Economics and Statistics. 56, 4: 457-74.

Estonian Institute of Economic Research (1997), Economic Indicators of Estonia,August 1997.

Gill, Andrew M. (1994), "Incorporating the Causes of Occupational Differences inStudies of Racial Wage Differentials." The Journal of Human Resources 29, 1:20-41.

Kandolin, Irja (1996), "Pay Differentials Between Men and Women in Estonia andFinland." The Finnish Review of East European Studies 3, 4: 56-78.

Kee, Peter (1995), "Native-Immigrant Wage Differentials in the Netherlands:Discrimination?" Oxford Economic Papers 47: 302-17.

Oaxaca, Ronald (1973), "Male-Female Wage Differentials in Urban Labor Markets."International Economic Review 14, 3: 693-709.

Rutkowski, Jan (1996), "High Skills Pay Off: The Changing Wage Structure DuringEconomic Transition in Poland." Economics of Transition 4, 1: 89-112.

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United Nations Development Programme (1996), Estonian Human DevelopmentReport 1996.

Vinokur, Aaron and Ofer, Gur (1987), "Inequality of Earnings, Household Income,and Wealth in the Soviet Union in the 1970s, in Politics, Work, and Daily Life in theUSSR: A Survey of Former Soviet Citizens, ed. James R. Millar, CambridgeUniversity Press, Cambridge, UK.


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The Outward Expansion of the LargestBaltic Corporations - Survey ResultsKari Liuhto1 & Jari Jumpponen2

AbstractA relatively high percentage of Baltic corporations have already started theiroperations abroad, over 40% of the companies studied. It is surprising that theapproaching EU membership does not seem to be the driving force of the Balticcorporations’ internationalization, though the EU is clearly the major exportdestination. The empirical evidence shows that the operations of the Balticcompanies in foreign markets, have concentrated on the ex-CMEA countries,especially on the former USSR. The empirical data indicates that most of theoperations abroad are related to marketing, such as the foundation of their ownrepresentative office or their own sales unit in a foreign market.

Key Words: Baltic States, international business, internationalization, Estonia,Latvia, Lithuania

1. INTRODUCTIONThe Baltic States are very small. Their population, even combined, is only 7.5million, which is less than the population of Austria. The small size of the Balticeconomies is emphasized, when their GDP is analyzed. In 2001, the GDP of all threeBaltic States, measured at purchasing power parity (PPP) was some USD 60 billion.Even the GDP of Ireland (USD 105 billion), which is among the least wealthy of EUmembers, is higher than total Baltic GDP. Finland's GDP was double that of theBaltics (CIA, 2002).

The small size of their economy obviously pushes Baltic companies abroad.Clear evidence of Baltic firms' internationalization at the macroeconomic level is thehigh exports-GDP ratio. In 2000, the exports of goods and services were some 45-91% compared to GDP, depending on the Baltic State in question (EU, 2002)3.

1Kari Liuhto, The Research Group for Russian and East European Business, Lappeenranta Universityof Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland.2Jari Jumpponen, The Research Group for Russian and East European Business, LappeenrantaUniversity of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland.

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The Outward Expansion of the Largest Baltic Corporations - Survey Results

In 1990, the overwhelming majority of Baltic States' foreign trade wasdirected to other socialist countries. Then, the CMEA covered over 90% of the BalticStates' exports (EBRD, 2000). Ten years later, the direction of the foreign trade hasalmost completely reversed. In 2001, the EU was the main trading partner of theBaltic States. Exports to the EU covered some 69% of the Estonian exports. Therespective share in Latvia was close to that of Estonia, but in Lithuania the EU sharewas remarkably lower, less than 50%. Also the imports from the EU are significant.The EU represented roughly 50% of the Baltic countries' imports, Estonia being themost dependent and Lithuania the least dependent on imports from the EU.(Foreign Trade, 2002)

Whilst the EU's importance in Baltic foreign trade has grown rapidly, thedependence on Russian trade has declined. In 2001, Russia covered only 3-10% ofBaltic exports, Estonia being the least Russia-oriented and Lithuania the mostRussia-oriented. Russia's proportion of Baltic imports is considerably higher thanthat of their exports. In Lithuania, the dependence on imports from Russia is by farthe highest, over 25%. In Estonia and Latvia, Russia formed just some 10% of thetotal imports (Foreign Trade, 2002).

Besides foreign trade flows, foreign direct investment (FDI) inflows verifythat the Baltic countries are open economies. In 2000, the FDI-inflow represented3-6% of Baltic GDP. The Baltic States have attracted much more FDI per capita thanother ex-Soviet republics. The cumulative FDI inflow per capita during 1989-2000in the Baltic States was on average over USD 1000, while in other former Sovietrepublics it was less than USD 170 (EBRD, 2001).

Finland and Sweden are the most important investor countries in Estonia,where they together form 65% of Estonian FDI stock in 2001. Denmark, in turn, isthe biggest investor in Lithuania. The Baltic States covered only a modest part of theFDI in another Baltic State. Only Estonia managed to climb among the top 10investor countries with a 6.5% FDI stake in both Latvia and Lithuania (see Table 12).

FDI has supported the recovery of the Baltic States from the transitionslump and has enhanced the improvement of enterprise competitiveness bothdirectly (foreign owner impact) and indirectly (via competition or copyingcompetitiveness). Along with the development of their competitiveness, the Balticcompanies have not only intensified their export activities, but they have also begunto invest outside their home market. In fact, a Latvian company was ranked thethird most international company among the Central and East European firms in2000 (see Table 1).

3According to the Economist Intelligence Unit, the exports/GDP ratio was in Estonia 90.6%, while inLatvia it was 44.9%, and in Lithuania 50.4%, respectively (EIU, 2002).


52

Table 1. Top 25 Non-Financial Transnational Corporation based in Central

Eastern Europe (ranked by foreign assets, USD mn, 2000)

Though only one Baltic company reached the top 25 list, the importance ofmonitoring the Baltic companies' expansion abroad is emphasized, due to theaccelerating globalization of business. Baltic corporations cannot simply afford tounderestimate the pressures created by globalization.

2.METHODOLOGYThe authors searched for answers to the following research questions:* To what extent have the largest Baltic companies already moved their

operations abroad?* What are the main driving forces behind internationalization?* What are the main target environments of internationalization?* What are the main operation modes used?

Due to limited research funds, the researchers were forced to limit the samplesize, and thus, they focused the study on the 100 largest companies in each BalticState. These 300 companies were selected on the basis of their net turnover/sales.

The authors deliberately decided to focus the study on the largestcorporations for three main reasons. First, should the researchers have aimed at

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random sampling, the outcome of the study would most probably have been lesssuccessful, because a large proportion of the registered enterprises do not operate.This would inevitably have caused an enormous non-response. Second, smallercompanies have usually less need, resources or skills for their internationalization.This would most probably have resulted in a great percentage of those answersindicating that the firm has not yet started its internationalization. Third, theinvestigation was focused on the largest companies, due to their economicimportance. The success of these companies' internationalization is crucial for theeconomic development of the Baltic States, since they form a significant part ofBaltic GDP and industrial production.

The questionnaire designed for the research deals with the reason,environment, and mode of the internationalization. The authors considered that thequestionnaire should be linguistically as clear as possible, to avoid the possibility ofmisunderstanding. It was also decided that the questionnaire should not exceed twopages and should not include overly sensitive issues, such as exact performanceindicators or ownership arrangements, since both a lengthy questionnaire andoverly sensitive questions would have reduced the Baltic managers' willingness torespond to the questionnaire (see Appendix 1).

The above methodological decisions proved to be correct, since theresponse rate was rather satisfactory, over one-third, especially taking intoconsideration that the mail survey was conducted among post-socialist companies,which are usually reluctant to reveal any information to researchers (see Michailova& Liuhto, 2000). In this context, it should be mentioned that due to the researchers'persistent efforts, two reminders, the response rate increased from 20% to 38% (seeTable 2).

Estonian companies were more active in participating in the survey thanLatvian and Lithuanian firms. Even if Latvian and Lithuanian corporations wereless enthusiastic about taking part in the research, the response is not so muchunbalanced by their lesser enthusiasm that the over- or under-representation of anycountry would distort the analysis on the internationalization of the largest Balticcorporations. The participating companies also represent various business fields ineach of the countries in question, so there is also no distortion in this issue (seeAppendix 2).

Analysis of returned questionnaires indicates that those questionnairesreceived by the researchers were accurately answered; though deficiencies could bedetected from questions concerning the geographical division of the exports. On thebasis of the response analysis, it can be assumed that using English in thequestionnaire did not result in an incorrect interpretation of the questions, and


54

thus, the received answers are believed to be valid and credible. Most probably, theresearch language did not cause the non-response as much as managers' hectictimetables or a fear of the data getting into the 'wrong' hands.

Table 2. The Response to the Survey

The questionnaires were first sent out on the 12th of January and the lastquestionnaire to be included in the analysis was received two months later, on the11th of March, 2001. Because firms from transition economies expand theiractivities abroad at an ever-increasing speed, the empirical data will becomeoutdated relatively fast, and therefore, it is extremely important to conduct follow-up studies frequently.

3. EMPIRICAL RESULTS

3.1. Exports of the Baltic CompaniesAlmost two thirds of the respondents (64%) indicated that their company hasexports. The export frequency among the Latvian corporations was considerablylower. Only one half of the studied Latvian companies have exports. When oneremembers that Latvian firms were more active in their activities abroad than theEstonian and Lithuanian ones, their lower export activity is rather puzzling.

The companies that have exports were asked to indicate the share ofexports out of their total sales. The data reveals that over one-third of the companieshave no exports, a fifth of the companies export less than one-fifth, and for the rest,exports compose 50% or more of the total sales (see Figure 1).

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Figure 1. The Share of Exports of Total Sales in the Baltic Companies (N=113)

The EU and other Baltic State(s) are the most common destinations for theexports. Of those companies that have exports, more than two-thirds export to theEU and/or to another Baltic State(s). The EU is especially favored among export-oriented companies i.e. if the proportion of the exports from the total sales is high,the company is likely to export to the EU. To put it differently, if a Baltic companyexports to the EU, it seldom has any other significant destinations for exports.Respectively, if a company exports elsewhere, the exports are divided betweenmany countries (see Figure 2).

Figure 2. The Division of Baltic Companies’ Exports to Other Baltic State(s)

and to the EU


56

Russia is the third most favored export destination, after the EU andanother Baltic State(s). The share of Eastern Europe was not high. In fact, the Balticcompanies export more to the USA than to Eastern Europe.

3.2. The Baltic Corporations' Operations AbroadIt is not exceptional to find a Baltic enterprise, which has already started itsoperations abroad. Some 42% of the studied companies have begun their operationsin a foreign market (see Table 3).

Table 3. Studied Baltic Companies Abroad

The table above shows that operations abroad are more common amongLatvian corporations than Lithuanian and Estonian ones. The empirical datacannot reveal any apparent explanation, as to why Latvian companies are moreactive in starting operations abroad than their Estonian and Lithuaniancounterparts.

The majority of Baltic companies stated that the driving force for theirinternationalization was getting a foothold in a larger economy. The option"internationalization is a necessity" was in second position. The aim of getting abetter price was the third most frequently selected alternative. Surprisingly,"preparation for EU accession" was selected by only 13% of those companies thathave operations abroad. All in all, it can be concluded that the domestic factorspushing Baltic companies abroad seem to be behind their internationalizationrather than the attractions of foreign markets per se (see Table 4).

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Table 4. Reasons for Baltic Companies to Operate Abroad (N=48)4

Though the EU has an important role as a destination for Baltic companies’exports, companies do not self-evidently seem to turn to the West in theiroperations. In fact, operations in other Baltic State(s) and in Russia are morecommon than operations in the EU (see Table 5).

Table 5. The Operations of the Baltic Companies Abroad (N=48)5

Starting operations in other Baltic State(s) is natural, as the Baltic Statesform a relatively familiar market place. Their geographical proximity can be anotherexplanatory factor. Estonian and Latvian companies, in particular, seem to havechosen to expand their operations in another Baltic State, while Lithuanian firmshave penetrated into other regions.

It is noteworthy to mention that also distant regions, like the United Statesand Asia, are represented among the environments where operations have beenstarted. Latvian companies, in particular, have discovered these 'remote'environments.

If one analyzes the reasons for internationalization and environmentselection together, an extremely interesting finding can be discovered. The EU is not

4 As a company may have several reasons for operating abroad, the sum of percentages exceeds 100%.5 As a company may have operations in many regions, the sum exceeds 100%.


58

regarded as "a larger economy", but Russia and the CIS are. In other words, Russiaand the CIS are selected if the Baltic corporation's goal is to search for a largermarket. The EU is chosen on a different basis.

The data clearly indicates that the largest Baltic companies do not prefer tostart production abroad. Similarly, joint ventures are not a very widely usedoperation mode. Instead, almost half of the companies with operations abroadindicated that they have their own representative offices (see Table 6).

Table 6. Baltic Companies’ Operating Modes Abroad (N=48)6

All in all, 32 of the studied companies indicated that they had activitiesabroad. 27 out of 32 corporations indicated that they have employees abroad.However, not more than two firms stated that they have the majority of their staffabroad. 28 companies announced they have assets abroad, but not more than sixcompanies have moved over 50% of their assets outside their country. Estoniancompanies have been more active than their counterparts in Latvia and Lithuaniain shifting their assets abroad (see Table 7).

As indicated in Table 1, only one Baltic company made the list of the 25most international companies in Central and Eastern Europe. The empiricalevidence of this study indicates that several other transnational Baltic companiesexist. The data also makes reference to the fact that the field of operation is not themain explanatory factor for moving assets and employees abroad. Rather, severaldifferent fields of operations can be detected behind these Baltic companies.

6 As a company may use many operation modes simultaneously, the sum exceeds 100%.

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Table 7.The Transnationality Analysis of the Studied Baltic Corporations

3.3. Future Operations Abroad Table 10 shows that only 28% of the companies studied planned to start operationsabroad. The data does not reveal a significant difference between Baltic companies'interest in beginning operations abroad in the future. Moreover, the answersindicate that the company's existing operations abroad do not seem to reflectwhether a company plans to start further operations abroad i.e. firms with noexperience in foreign operations are planning to start operations abroad (15%) asfrequently as those enterprises with experience (13%).

7 The transnationality index is calculated as the average of three ratios: foreign assets to total assets,foreign sales to total sales and foreign employment to total employment.


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Table 8. The Companies’ Intentions to Operate Abroad

While eleven companies indicated that they have plans to expand theiroperations in another Baltic State, only seven companies mentioned the EU as thetarget environment. In fact, Russia was more popular than the EU. Nine companiesplanned to start their operations in the EU. Keeping in mind the small number ofthe response, the empirical evidence tentatively indicates that the largestcompanies in the Baltic States perceive the EU as a trading partner rather than adestination for their expansion.

4. CONCLUSIONOver 60% of the studied enterprises claimed to have exports. A relatively highpercentage of firms (40%) indicated that they have started operations abroad.These high percentages do not come as surprise, since the Baltic States are smallmarkets, which automatically push most of the largest Baltic corporations abroad.

Some 60% of the companies indicated that a foothold in a larger economywas one reason for starting operations abroad. The second most frequently givenanswer (over 50%) was "internationalization is a necessity to survive in futurebusiness". Third, Baltic corporations expand their activities in foreign markets toreceive a better price for their commodity.

These responses could be easily anticipated, but it is very surprising thatpreparation for EU accession did not rank higher among the reasons for startinginternationalization. The response of the Baltic managers indicate that approachingEU membership is not the driving force for Baltic corporations'internationalization, even though the EU is clearly the major export destination.

Baltic corporations' management may think that they are able to maintainsales to the EU even without starting-up their own operations inside the EU. In away, maintaining production inside the Baltic States can be a rational decision sinceit allows Baltic corporations to take advantage of lower production costs whileenjoying the benefits of the European Single market. On the other hand, EU

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membership may attract more EU and even non-EU companies to the Baltic States,and thus increase competition inside the Baltics.

Consequently, increasing competition will force Baltic companies toimprove their effectiveness, either via increasing their size or by sharpening theirfocus. If the Baltic corporations do not manage to improve their competitiveness,we could witness an increase in bankruptcies, mergers and takeovers in the BalticStates over the next decade (see Table 9).

Table 9. Summary of the Empirical Findings

REASON FOR INTERNATIONALIZATION: WHY INTERNATIONALIZE?10 main reasons behind the Baltic corporations’ internationalization:

1) Small domestic market forces the Baltic companies abroad (small economy-relateddriving force).

2) Survival in future business requires internationalization (a global trend in business).3) Baltic firms expect to receive a better price for their commodity abroad (relatively

low buying power in the post-socialist countries).4) Securing a resource supply (the Baltic States are relatively poor in natural resources).5) Baltic companies are searching for less competitive markets, especially in other

former Soviet republics (inter-enterprise competition seems to be fiercer in theBaltic States than in other ex-Soviet republics).

6) Baltic firms are searching for more stable markets in the West (to increasepredictability in their enterprise development).

7) Foreign ownership in the company influences their internationalization decision(internal driving force)

8) Domestic clients have expanded their operations abroad (following the own clientprinciple).

9) Preparation for EU accession (a need for Pan-European "internationalization").10) Logistical reasons have attracted Baltic companies abroad (a goal to improve

efficiency).

ENVIRONMENT SELECTION: WHERE TO INTERNATIONALIZE?5 main environments, where Baltic firms have started their operations:1) The Baltic market is the key foreign environment (a familiar and close foreign market).2) Russia’s potential is attractive (earlier business relationships and experience).3) The EU has attracted surprisingly few Baltic companies to start their operations

there, though the EU is the main export direction (a fear of competition or EU regulations?).

4) Other ex-Soviet republics (earlier experience and less-fierce competition).5) Eastern Europe (Baltic products’ price-quality ratio suit both East European demand

and buying power).

MODAL CHOICE: HOW TO INTERNATIONALIZE?* Various marketing operations dominate (to increase sales, while keeping financial

investment low).* Subcontracting, licensing, franchising (minimizing risks, while penetrating a foreign

market).* Joint venturing is a mode, allowing partners to join their resources and knowledge.* Their own production unit abroad is still a relatively rarely used operation mode.* Acquisition of a foreign company is still a rare option, mainly due to financial

constraints.


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The empirical evidence shows that the operations of the Baltic companiesin foreign markets have concentrated on the ex-CMEA countries, especially in theformer USSR. The The empirical evidence shows that the operations of Balticcompanies in foreign markets have concentrated on the ex-CMEA countries,especially in the former USSR. The explanation for focusing on the ex-CMEAmarket may stem from the fact that the Baltic commodities' price-quality ratiobetter fits these markets than those of the developed West. Also, their earlierbusiness relations and experience in these markets may have offered a competitiveadvantage to the Baltic corporations, compared to their Western rivals.

The empirical evidence supports the presumption that most of theoperations abroad are related to marketing, such as establishing their ownrepresentative office or their own sales unit in a foreign market. These sales-increasing activities are a logical modal choice since they do not require heavyfinancial investment. It can be assumed that operational modes, which requiremore investment and risk taking, will increase along with the improvement of theBaltic firms' financial position.

In closing, it can be argued that internationalization is a necessarycondition, though not a sufficient condition by itself, for securing the Balticcorporations' survival in future business. Therefore, Baltic corporations must buildstrategic alliances with each other or foreign companies in order to be able to copewith the competitive pressures arriving both from the EU and from the East, as itcan be predicted that Russian companies will intensify their investment activities inthe Baltic States in years to come.

Until now, Russian investments in the Baltics have remained relativelymodest (see Table 10). In Latvia, Russia formed some 5% of the FDI stock in 2001.Both in Estonia and Lithuania Russian investments represented only some 1-2% ofthe FDI stock. However, it would not be a surprise if Russian companies decided touse the Baltic States as a familiar foothold to the EU single market, and would thusdecide to increase their investments in the Baltic States before the Baltic Statesreceive their EU membership (Liuhto & Jumpponen, 2002).

PECULARITIES CONCERNING THE BALTIC FIRMS’ INTERNATIONALIZATION:* Despite the EU dominance in exports and the approaching EU membership of the

Baltic States, surprisingly few Baltic firms have started their operations within thecurrent EU.

* The ex-socialist bloc clearly dominates as an environment, where foreign operationshave been started.

* The majority of Baltic firms are not planning to start operations abroad in the nearfuture.

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Table 10. Foreign Direct Investment Stock in the Baltic States

by Investing Countries


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REFERENCES

Bank of Estonia (2002) Direct investment stock by countries, available athttp://grizli.ml.ee/itp1/itp_report.jsp [referred 6.11.2002]

CIA (2002) http://www.odci.gov/cia/publications/factbook/fields/2001.html[referred 6.11.2002]

EBRD (2000) Transition Report 2000, European Bank for Reconstruction andDevelopment, London.

EBRD (2001) Transition Report 2001, European Bank for Reconstruction andDevelopment, London.

EU (2002) Regular Report, The European Union, Brussels.

Foreign Trade (2002), Estonia, Latvia, Lithuania Foreign Trade 2001, Statisticaloffice of Estonia, Tallinn.

LCB (2002) Latvian Central Bank, Balance of Payments database, available at:http://www.bank.lv/izdevumi/Latvian/maksbil/2002-02/LMB5.xls [referred6.11.2002]

Liuhto Kari & Jumpponen Jari (2002) ‘International Activities of RussianCorporations - Where Does Russian Business Expansion Lead?’, Russian EconomicTrends.

Michailova Snejina & Liuhto Kari (2000) ‘Organisation and Management Researchin Transition Economies: Towards Improved Research Methodologies’, Journal ofEast-West Business 6/3.

Statistics Lithuania (2001) Statistical Yearbook of Lithuania 2001.

UNCTAD (2002) World Investment Report 2002, United Nations, New York.

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APPENDIX 1. The Questionnaire

1. Does your company have exports (mark appropriate alternative with X)?

( )Yes ( ) No - if no, move to question 4.

2. The share of exports of total sales?

( ) 1-5% ( ) 6-10% ( ) 11-20% ( ) 21-30% ( ) 31-40% ( ) 41-49%( ) 50-60% ( ) 61-70% ( ) 71-80% ( ) 81-90% ( ) 91-99% ( ) 100%

3. What is the share of the following markets of your company's exports?

EU ____ %Another Baltic State ____ %Russia ____ %Other ex-Soviet republic/s ____ %Eastern Europe ____ %USA ____ %Asia ____ %Other, what ____________________%

4. Does your company operate abroad (not taking into account exports)?

( )Yes ( ) No - if no, move to question 7.

5. Which operation mode/s is your company using abroad (many answers

possible)?

( ) Marketing co-operation with a foreign firm/s( ) Own representative office/s( ) Own sales unit/s( ) Joint venture with another firm( ) Completely owned production unit/s( ) Equity ownership in a foreign company/ies( ) Own investment / holding company abroad( ) Subcontracting / licensing / franchising agreement with a foreign company ( )Other,what ______________________________________________


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6. In which regions your company has started business operations (not

exports)?

EU ( )Yes ( ) NoAnother Baltic State ( )Yes ( ) NoRussia ( )Yes ( ) NoOther ex-Soviet republic/s ( )Yes ( ) NoEastern Europe ( )Yes ( ) NoUSA ( )Yes ( ) NoAsia ( )Yes ( ) NoOther, what -________________________( )Yes

7. What is the share of the following activities of your company's

performance?

Home market AbroadAssets ___________ % ______ %Sales ___________ % ______ %Employees ___________ % ______ %Profits ___________ % ______ %

8. What are the reasons why your company has started operations abroad?

( ) To get a foothold in a larger economy( ) To get a better price ( ) Production costs are lower abroad( ) To decrease transportation costs( ) To secure availability of raw materials or skilful labor( ) To avoid / to reduce custom duties or other tariffs( ) To reduce tax burden ( ) Due to investment incentives offered by host or home government( ) Due to more stable business environment( ) Due to better business infrastructure ( ) Domestic clients have started their operations abroad( ) Influence of foreign owner in your company's management ( ) Competition is not so hard abroad as in the home market( ) Preparation for the accession of your country in the EU( ) Internationalization is a necessity to survive in the future business( ) Other, what___________________________________________

9. Are you planning to start operations abroad (not exports)?

( )Yes , when_____________ ( ) No - if no, move to the end of thequestionnaire.

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10. In which regions you are planning to start operations (not exports)?

EU ( )Yes ( ) NoAnother Baltic State ( )Yes ( ) NoRussia ( )Yes ( ) NoOther ex-Soviet republic/s ( )Yes ( ) NoEastern Europe ( )Yes ( ) NoUSA ( )Yes ( ) NoAsia ( )Yes ( ) NoOther, what ______________( )Yes

Thank you for your valuable contribution! If you wish to receive the researchreport on the internationalization of the 300 largest Baltic companies, please writeyour company's address below or enclose your business card in the reply letter.________________________________________________________________________________________________________________________________________________________________________________________


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APPENDIX 2. The Sample8

ESTONIACompany Field Net sales(1000 EEK)9

1. Eesti Energija AS Energy 3 923 7222. Eesti Telefon AS Telecommunication 2 404 5773. Hansatee Grupp AS Transport 1 825 7724. Eesti Mobiiltelefon AS Telecommunication 1 469 5975. Eesti Põlevkivi AS Mining 1 455 4086. Eesti Raudtee AS Transport 1 401 3987. Sylvester Grupp AS Wood processing 1 070 9398. Tallinna Kaubamaja AS Trade 996 0799. Kreenholmi Grupp Textiles 984 84610. Pakterminal AS Transit 978 64411. ETK Hulgi AS Trade 878 20512. Neste Eesti AS Oil - petroleum trade 874 91813. Tallinna Soojus AS Energy 822 50414. Kaupmees & Ko AS Trade 812 14015. Tallinna Sadam AS Port services 803 73216. Stockmann AS Trade 793 73617. Eesti Gaas AS Energy 762 64618. Balti Laevaremonditehase AS Shipbuilding 761 26419. Merko Ehitus AS Construction 733 65720. Estonian Air AS Transportation 720 98121. Saku Õlletehase AS Beverages 715 04022. EMV AS Construction 686 73523. Bankend Eesti AS Trade 652 23724. Onako Eesti AS Oil - petroleum trade 646 99025. Premium Oil AS Oil - petroleum trade 627 32026. E.O.S. AS Transit 621 62727. Eesti Statoil AS Oil - petroleum trade 592 46628. Estline AS Transport 590 72929. EE Grupp AS Construction 590 72730. Stora Enso Mets AS Wood processing 586 86531. Fanaal AS Building materials 582 39332. NT Marine AS Services for ships 578 32133. Eriõli Kaubanduse AS Oil - petroleum trade 553 53434. Viru Keemia grupp AS Chemicals 552 53835. Rakvere Lihakombinaat AS Foodstuffs 539 55536. Eesti Metallieksport AS Metal trade 531 159

8 The companies marked with bold returned a usable reply and companies marked in the brackets werenot included in the research either because the researchers were not able to find the company's mailaddress. Also corporations operating in banking or in insurance business were dropped out of thesurvey. Moreover, the Latvian Privatization Agency was no approached. 9 In February 2001, one US dollar equaled 16,9 Estonian kroons (EEK).

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37. FKSM AS Port services 524 32638. Norma AS Automobile seat belts 516 40039. Eesti Post AS Post services 507 60240. G.S.G. AS Oil - petroleum trade 507 31641. ETK Maksimarketi AS Trade 499 08442. Saurix Petroleum AS Oil - petroleum trade 496 40943. Falck Baltics (ESS AS) Security services 480 54744. Tallinna Külmhoone AS Foodstuffs 454 98845. Eesti Mereagentuur AS Stevedoring 438 83046. Jungent OÜ Trade 430 15147. Tallinna Vesi AS Utilities 426 80348. S-Marten AS Trade 408 11649. Elcoteq Tallinn AS Electronics 403 66350. Rannila Profiil AS Building materials 400 15051.(AVR Trans AS Transit 390 718)10

52. Tamro Eesti Pharmaceuticals 388 93053. Estravel AS Travel services 388 50054. Eesti Coca-Cola Joogid AS Foodstuffs 382 19555. JOT Eesti OÜ Electronics 380 14456. Horizon Tselluloosi ja Paberi AS Paper production 379 04257. Kunda Nordic Tsement AS Building materials 373 96758. Tallinna Piimatööstuse AS Dairy 366 74859. Maseko AS Foodstuffs 366 92360. SI-Kaubabaasi Trade 350 95961. Kalev AS Confectionery production 338 51162. ES Sadolin AS Building materials 332 41763. Famar-Desl AS Trade 329 87664. Toyota Baltic AS Trade 326 27765. Repo Vabrikud AS Wood processing 325 79366. Kesko Eesti AS Trade 325 46067. Veho Eesti AS Trade 323 16768. Hiiu Kalur AS Foodstuffs 314 72169. HTM Sport Eesti OÜ Sport equipment 309 21370. Siemens AS Electronics 308 16871. Silmet Grupp AS Chemicals 305 21372. Tallegg AS Foodstuffs 303 77273. Metsind AS Timber products 292 24774. Silberauto AS Trade 291 01875. Baltika AS Beverages 288 92676. Ericsson Eesti AS Trade 283 36277. Tech Data Eesti AS Information technology 282 02478. Mets & Puu AS Forestry 281 40579. Marat AS Textiles 279 964

10 The companies marked in the brackets were not included in the research either because theresearchers were not able to find the company's mail address. Also corporations operating in bankingor in insurance business were dropped out of the survey. Moreover, the Latvian Privatization Agencywas no approached.


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80. Rapla Dairy Dairy 279 15781. ABB AS Energetics 274 88182. Liviko AS Alcohol products 258 81383. Radiolinja Eesti AS Telecommunication 253 41984. Nitrofert AS Chemicals 253 15185. (NB Oil Group OÜ 248 427)86. Nordic Jetline AS Travel services 248 40687. AbeStock AS Wholesale trade 243 28588. TVMK AS Wood processing 242 55189. Forestex AS Wood trade 239 16190. Valga Liha- ja Konservitööstus AS Foodstuffs 236 66291. Saksa Auto AS Vehicle trade 235 70692. Microlink Arvutite AS Information technology 235 37093. Tartu Õlletehas AS Beverages 235 13294. Skanska Ehituse AS Construction 232 65195. Holmen Mets AS Trade 227 00596. Tarmeko AS Furniture manufacturing 226 28397. Baltex 2000 AS Textiles 224 96398. EVR Koehne AS Construction 224 84399. Teede REV-2 AS Construction 224 061100. Amisco AS Shipping services 220 049101. Kommest Auto AS Trade 219 648102. Södra Eesti AS Paper products trade 216 447

APPENDIX 2. ContinuedLATVIACompany Field Net Turnover(LVL million)11

1.Latvenergo PVAS Energy 167,562.Lattelkom SIA Telecommunication 129,303.Latvijas kugnieciba PVAS Shipping 111,794.Latvijas dzelzcels VAS Transport 110,725.Turiba CS Trade, catering 84,006.Latvijas Gaze AS Energy 83,087.(Latvijas Privatizacijas agentura VAS Privatization 70,04)8.Kurzemes degviela AS Oil products 67,779.Latvijas Mobilais telefons SIA Telecommunication 64,6010.Rigas siltums AS Heating 63,9511.Liepajas metalurgs AS Metal industry 56,6412. Latvija Statoil SIA Oil products 53,0013.Latvijas finieris AS Woodworking 50,1014.Ventpils nafta AS Transit services 45,8615.Ventpils tranzita serviss SIA Oil transit 44,9216.Procter & Gamble Marketing Latvia SIA Trade 43,4717.Alianse-2 SIA Trade, foodstuffs 38,73

11 In February 2001, one US dollar equaled 0,62 Latvian lats (LVL).

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18.Dinaz SIA KU Oil products 31,1419.(Latvijas unibanka AS Finance 30,98)20.Nelss SIA Woodworking, trade 29,3721.(Parekss banka AS Finance 28,16)22.Bravo SIA Trade, beverages 27,9123.Severstallat AS Steel trade 27,5724.Neste Latvija SIA Oil products 23,3625.Interpegro Latvija SIA KU Trade 23,3026.(Latvijas Banka Finance 22,96)27.Air Baltic Corporation AS Transport 22,7428.Rimi - Baltija SIA Retail trade 22,2029.LUKoil Baltija R. SIA Oil products 21,9230.LatRos Trans SIA KU Oil transit 21,0631.Lex-U SIA Trade, foodstuffs 20,8632.Lindeks AS Wood trade 20,0133.Greis SIA Trade 19,9934.Skonto buve SIA Construction 19,7835.Latvijas pasts VAS Post service 19,4936.Aldaris AS Beverages 19,3537.BMGS AS Construction 19,1038.Tamro SIA Trade, medicines 18,0139.Rigas udens PU Municipal services 17,7040.Linda SIA Wood trade 17,6541.(Hansabanka AS Finance 17,45)42.Ogre AS Textiles 17,2543.Venceb AS Construction 17,0044.Krasainie lejumi AS Metal working 16,8645.Skonto metals SIA Metals 16,4946.Tramvaju un trolejbusuparvalde PU Transport 16,3847.Elko Riga SIA KU Computers, trade 16,0348.Mono SIA Trade, insurance 15,8449.(Balta AAS Insurance 15,77)50.Ventbunkers AS Transit services 15,7751.Rigas piena kombinats AS Dairy 15,3352.Riga kugu buvetava AS Mechanical engineering 15,1953.Viada SIA Oil products 15,1054.Baltkom GSM SIA KU Telecommunications 15,0855.Laima AS Foodstuffs 15,0056.Shell Latvia SIA Oil products 14,8057.Unilever Baltic LLC SIA Trade 14,6258.Latvijas Balzams AS Beverages 14,6059.Augstceltne SIA Oil products 14,5160.Preses apvieniba AS Trade 14,0261.Lauma AS Textiles 13,6562.Weeluk (Baltic) Ltd. SIA Wood export 13,6463.Ventspils ekspedicija SIA Transit services 13,6064.Cido partikas grupa SIA Foodstuffs 13,37


72

65.Valmieras stikla skiedra AS Chemical industry 13,3366.(Latvijas krajbanka Finance 13,11)67.Alkolats SIA Beverages, trade 13,0068.Karsten Latvian SIA Trade 12,8269.Rigas vini AS Beverages 12,8070.Lido nafta SIA Oil products 12,5471.Nelss TT Trade 12,1772.Silva SIA Forestry 12,0673.Unifex SIA Trade 11,8774.Skanska konstrukcija SIA Construction 11,8075.Latvijas nafta PVAS Oil products 11,7876.Dobeles dzirnavnieks AS Foodstuffs 11,7077.Baltimar VT SIA Oil products 11,6978.Fortech SIA Computers 11,5979.SEL – II SIA Beverages, trade 11,5980.Hanzas maiznicas AS Foodstuffs 11,1681.Baltfor SIA Wood export 11,1082.Klangu kals SIA Fuel trade 11,0883.Rigas piensaimnieks AS Dairy 10,9184.Motors Latvia SIA Cars 10,8585.CHS Riga SIA Computers, trade 10,8086.Kurekss SIA Wood export 10,7387.Cido logistika SIA Foodstuffs, trade 10,6088.Siemens SIA Electronic equipment 10,5089.Ventamonjaks AS Transit services 10,2890.(Austrumu alianse AAS Insurance 10,22)91.Tolaram Fibers AS Chemical industry 9,8892.Grindeks AS Pharmaceuticals 9,8593.Diena AS Publishing 9,8494.Lattransrail SIA Construction, transport 9,8095.Oilands SIA Fuel trade 9,8096.Nokia Latvija SIA Telecommunications 9,7297.Ventspils tirdzniecibas osta AS Stevedores 9,7098.Rigas transporta flote AS Shipping 9,5499.Juraslicis AS Fish industry 9,50100.Nelda SIA Trade 9,50101.Rimako AS Textiles 9,50102.Ziemelu nafta SIA Oil products 9,50103.(Stalkers AS Trade 9,48)104.Latvijas Gaisa satiksme VAS Air navigation 9,43105.Philips Latvija SIA Trade 9,40106.Bolderaja Woodworking 9,32107.Kalija parks AS Port services 9,32108.Baltijas transporta apdrosinasana AASInsurance 9,31109.Jelgavas cukurfabrika AS Sugar producer 9,20110.(SBV SIA Construction 9,18)111.Rezeknes piena konservu kombinats AS Dairy 9,03

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APPENDIX 2. ContinuedLITHUANIACompany Field Sales(LTL)12

1. Mazeikiu nafta Oil - petroleum products 2.283.923.7972. Lietuvos Energija Electric utilities 1.468.362.1093. Lietuvos Telekomas Telecom 969.493.5114. Lietuvos Dujos Natural gas utilities 555.796.4745. Lietuvos Gelezinkeliai Transport 555.036.2576. Lifosa Chemicals 494.171.0307. Achema Chemicals 339.954.1258. Ekranas Electrical engineering 279.361.1669. Lietuvos juru laivininkyste (Lisco) Shipping 227.876.16410. Rokiskio Suris Dairy 224.573.00011. Kraft Foods Lietuva Food 214.940.93312. Lietuvos Avialinijos Transport 196.718.83413. Kauno Energija Heating 190.423.31414. Alytaus Tekstile Textile 170.637.18715. Lietuvos Kuras Oil - petroleum products 164.145.68116. Snaige Electrical engineering 149.903.87217. Pieno Zvaigzdes Dairy 147.098.79618. Birzu Akcine Pieno Bendrove Dairy 145.811.21019. Zemaitijos Pienas Dairy 138.682.50120.Dirbtinis Pluostas Chemicals 121.059.95221. Akmenes Cementas Building materials 112.559.709 22. Utenos Trikotazas Clothing 109.000.97323. Kauno Tiltai Construction 106.210.61924. Klaipedos Nafta Shipbuilding 105.044.71625. Klaipedos Maistas Oil - petroleum products 102.128.31726. Klaipedos Juru Kroviniu Kompanija Stevedoring 98.318.85227. Kalnapilis Brewery 96.789.18428. Kausta Construction 96.191.72529. Vilniaus Duona Confectionery & bread 95.779.95230. Alkesta Construction 94.627.69131.Klaipedos Energija Heating 92.372.77332.Klaipedos Mediena Wood products 91.013.54633.Panevezio Keliai Construction 87.793.62534.Panevezio Silumos Tinklai Heating 85.781.63135.Baltik Vairas Vehicles 85.543.15836.Baltijos Laivu Statykla Shipbuilding 83.609.47837.Marijampoles Pieno Konservai Dairy 81.336.82538. Vilniaus Vingis Electrical engineering 81.225.03439. Alita Drinks 79.095.63640. Alytaus Silumos Tinklai Heating 78.794.47041. Drobe Textiles 76.211.074

12 In January 2001, one US dollar equaled 4,00 Lithuanian litas (LTL).


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42. Linas Textiles 76.112.35043. Anyksciu Vynas Drinks 75.550.46844. Stumbras Drinks 74.315.80045. Svyturys Brewery 73.714.24946. Panevezio Statybos Trestas Construction 72.743.82247. Panevezio Pienas Dairy 72.107.30948. Kretingos Grudai Cereals 72.089.32649.Mesa Meat products 70.956.60450.Klaipedos Transporto Laivynas Shipping 67.924.971 51.Ventus-Nafta Oil – petroleum products 66.633.65752.Vilniaus Paukstynas Meat products 65.722.54753.Kauno Grudai Cereals 65.274.33954.Lytagra Trade 62.286.71255.Vievio Paukstynas Meat products 61.690.45656.Viti Construction 59.391.88657.Utenos Pienas Dairy 57.551.36658.Klaipedos Pienas Dairy 57.056.90359.Siauliu Energija Heating 55.715.99460.Grigiskes Paper and printing 55.032.09561.Montuotojas Construction 53.707.84162.Hidrostatyba Construction 52.531.90563.Siauliu Plentas Construction 52.208.12464.Alna Computer technologies 50.439.33065.Apranga Trade 50.140.48466.Medienos Plausas Paper and printing 49.090.87467.Plasta Plastics 46.334.62468.Klaipedos Baldai Furniture 46.259.30069.Vilniaus Pergale Confectionery and bread 43.654.17070.Krekenavos Agrofirma Meat products 43.438.29571.Malsena Cereals 43.212.91872.Siauliu Stumbras Leather, leather products 42.436.38273.Kaisiadoriu Paukstynas Meat products 40.888.93874.Vilniaus Mesos Kombinatas Meat products 40.825.48275.Dvarcioniu Keramika Building materials 40.609.23776.Klaipedos Duona Confectionery and food 38.554.06077.Levuo Trade 37.451.68278.Siauliu Pienas Dairy 36.314.34479.Lietuvos Tara Packaging 35.726.23380.Vernitas Chemicals 34.722.02381.Kauno Pienas Dairy 34.168.13382.Nemunas Building materials 33.952.17383.Ragutis Brewery 32.841.35784.Vilniaus Tauras Brewery 32.667.87985.Metalu Komercija Trade 32.320.75686.Vilniaus Baldu Kombinatas Furniture 31.846.51587.Audejas Textiles 31.759.83388.Liteksas Textiles 31.551.686

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89.Skaites Electrical engineering 30.758.27690.Kauno Ketaus Liejykla Building materials 30.080.70491.Aliejus Oil production 29.722.94892.Silutes baldai Furniture 29.432.64393.Vilniaus Degtine Drinks 29.394.46594.Ekinsta Construction 29.054.62295.Kuro Aparatura Electrical engineering 28.356.01896.Kelmes Pienine Dairy 28.129.65397.Satrija Clothing 28.124.63298.Siulas Textiles 27.910.37899.Kedainiu grudai Cereals 26.362.957100.Naujoji ruta Confectionery and bread 25.971.625


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BERTOLA G, BOERI T and NICOLETTI G (eds) Welfare and Unemployment in aUnited Europe: A Study for the Fondazione Rodolfo Debenedetti MIT 2001

Tito Boeri (general editor of the volume) writes in his introduction ‘This bookaims at putting the current debate on the future of (social) welfare and employment ina united Europe on a new footing. We need fewer apocalyptic statements, fewerCassandras, more facts, and deeper theoretical perspectives’ (p 1) It has to be said thatthe book does indeed live up to such a billing. Largely this is because, in contrast tomany edited volumes that feature numerous disparate contributions, often of variablequality, here we are offered two extensive and solid pieces of work by top-flightresearchers.

The overall theme of the book is the impact on social welfare andunemployment of more than twenty years of unprecedented integration in Europe. Thefirst study, by Guiseppe Bertola, Juan Francisco Jimeno, Ramon Marimon andChristopher Pissarides (Bertola et al), deals with the past, present and future of EUwelfare systems. The second is authored by Guiseppe Nicoletti, Robert Haffner, StevenNickell, Stefano Scarpetta, and Gylfi Zoega (Nicoletti et al) and aims to analyse theinterrelationships between product and labour market liberalization or deregulation.The quality extends to the commentary and discussion. Thus, in addition to hisinteresting introduction Tito Boeri gives a brief summing-up and there are commentson the first study by Charles Bean and Goesta Esping-Andersen and on the second byOlivier Blanchard and Andre Sapir.

Bertola et al show that the social welfare systems of the EU remain remarkablyheterogeneous despite the overall progress of integration in EU goods and factormarkets. They identify four broad welfare systems within the EU corresponding todistinct groups of countries. The Nordic group, which includes the Netherlands as wellas Denmark, Finland and Sweden, has a tradition of universal welfare provision. At30% or more of GDP these countries have the highest levels of social protectionexpenditures in the EU, they have rather generous unemployment benefits togetherwith an important role for active labour market policies. The Nordic group also has arather low degree of income inequality both in terms of earnings and when adjusted fortaxation and transfers.

Another group, described as the Continental group, comprises Austria,Belgium France and Germany. At 27% to 30% of GDP total social expenditures aremarginally lower than in the Nordic countries but income inequality is somewhathigher. These are countries regarded as being in the Bismarkian tradition wherebenefits are linked to employment, wage determination is centralised, and employmentprotection legislation is rather stringent.

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Next come the Anglo-Saxon countries. Effectively, this means the UK andIreland. These are regarded as being in the Beveridgian tradition with targeted benefits,a high degree of income inequality, low unemployment benefits, weaker employmentprotection legislation, decentralised wage-setting, and minimal active labour marketpolicies.

Finally, the Southern European countries have more recently developedwelfare states and, at 20% plus or minus a few points, have the lowest levels of socialprotection expenditures (arguably, Ireland with only 18.1% according to Table 1.1should also be in this group), and high income inequality (not much influenced itappears by transfers. A notable feature of the Southern countries, that is mentioned butnot really researched, is the role played by the family in their social protectionarrangements. The family would also very likely be rather important in the context of,say, the Baltic states.

Having categorized the various national systems Bertola et al discussdevelopments and future options. They note that fears of a so-called ‘race to the bottom’in terms of national social protection in the face of increased mobility of capital andlabour have not so far materialized. They examine the relationship between the welfaresystems and economic performance, concluding that ‘the welfare state appears toachieve its intended poverty-reduction purpose with no obvious adverse effects onincome levels or growth rates’ (p 67). Charles Bean in his comment regards this view as‘rather sanguine’ and points out that Bertola et al may have underestimated the growtheffects of alternative systems of provision by focusing only on the labour market. Henotes that ‘the most obvious omission here is the effect of unfunded pension schemeson national savings and investment rates’ (p 124).

Bertola et al conclude that while the diversity of European welfare states hashitherto proved rather durable, it may not be fully sustainable in the long run.Accordingly, they propose a basic safety net that is provided at the EU level, with aspecific budget-line in the EU budget. They regard the associated problems of cost-of-living differentials as difficult but ‘technical’. As for contingent insurance provision,Bertola et al believe that this should address real market failures, participation shouldbe mandatory and minimum contribution levels should be coordinated at the EU level.They also regard it as important that the link between contributions and benefitsshould be emphasised.

Finally they note a whole category of ‘local’ social provisions that shouldproperly remain organised at the appropriate local level.

Nicoletti et al first explore the degree and dynamics of price convergence in theEU in the wake of deeper integration. For this they construct similarity indices, based


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on the Grubel-Lloyd index of intra-industry trade. It appears that price similarity in theEU for all products increased by 2.6 percentage points between 1985 (when the indexwas 81.9) and 1996 (when it was 84.6). Not all sectors experienced price convergenceover the period. Most notably, in the energy sector prices diverged by 5.0 points overthe period. However, it is interesting that the prices of EU non-tradables did convergeand at a faster rate than the prices of tradables. Other interesting findings are thatprices are more similar in the EU than they are in the other OECD countries and theyhave tended to converge faster. However, curiously, the rate of EU price convergenceslowed down after 1992.

The remainder of Nicoletti et al is devoted to examining the impact of productmarket deregulation on labour markets. Partial theoretical analysis suggests that anincrease in product market competition shifts the demand for labour outwards andthereby has a positive effect on employment, but at the same time if labour markets areunionized wages would tend to fall. On the other hand, general equilibrium effects aremore complicated – some arguments suggest that an economy-wide increase inproduct market competition would increase both wages and employment while otherarguments suggest that both wages and employment might fall as competitionintensifies in product markets.

The empirical evidence is complex, not least because measurement of thedegree of both product and labour market regulation is difficult. Nicoletti et al constructregulatory indicators that are then used to examine the interrelationship betweenregulation and labour market performance. Boeri sums up their results as showing ‘thatrestrictive product-and labour-market are closely interlinked. Environments moreprone to competition in the product markets also tend to offer more protection to labor-market insiders and vice versa’ (p 252). Simultaneous regulatory reform in both sets ofmarkets can deliver improved economic performance. Hence Nicoletti et al suggest thatsuch reforms should be coordinated. Boeri points out that this implies a case forextending EU level policy making to labour markets ‘at least to match [that] prevailingin product markets’ (p 253).

This book will repay study by researchers in the Baltic states and otherEuropean transition economies. Transition together with the EU accession process canbe represented in terms of very much the kind of developments analyzed here.Accordingly, although not dealing explicitly with Eastern Europe or enlargement thebook is a rich source of ideas for labour market and social welfare research in thetransition economies. Definitely something to have on the reference shelf.

ALF VANAGS BICEPS1

1 Director, Baltic International Centre for Economic Policy Studies. E-mail: [email protected]

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KEUSCHNIGG, M Comparative Advantage in International Trade: Theory andEvidence, Physica-Verlag, 1999

Paul Samuelson once remarked that the principle of comparative advantagewas one of the few propositions in economics that was both non-trivial and true.What Samuelson meant in this instance was that the theory of comparativeadvantage is logically correct. Demonstrating ‘empirical truth’ is another matterand pursuit of the empirical validity of the theory of comparative advantage hasgenerated a whole industry of activity by economists and econometricians.

This is especially true of the Heckscher-Ohlin (HO) version of the theory inwhich it is asserted that comparative advantage is determined by relative factorendowments. Leontief started the ball rolling in 1953 when he unveiled his famous‘paradox’ in which it was proposed that either the United States was abundant inlabour relative to the rest of the world or the HO theory was empirically false.

Leontief’s challenge to received theory has subsequently been taken up bymany economists and this volume, which is one of a series devoted to empiricaleconomics, offers a useful summary of both empirical and theoretical responses tothe Leontief paradox.

Although Leontief’s original study was conducted within the framework ofthe two-good, two-factor model familiar to students of international trade (the two-country aspect of the standard HO model was implicit in Leontief) it is ratherobvious that serious empirical investigation of the determinants of internationaltrade patterns needs to address the reality that the world is populated by manygoods and many factors. In a many-factor, many-commodity world, the neatness ofthe 2x2x2 model evaporates and ‘when the number of goods exceeds that of factors,the precise commodity pattern of production and trade is indeterminate’ (p 13).

This is Mirela Keuschnigg’s starting point. She outlines a compact fashionthe HO model as developed by Vanek (known as the HOV version of the model) inwhich the spirit of the original is retained in the form of a proposition that relatesthe factor content of a country’s net trade to its relative factor endowments. Thusthe HOV model postulates that international trade in goods implicitly trades factorsof production and that a country will ‘export’ factors in which its world endowmentshare exceeds it consumption share, and ‘import’ those in which it consumes morethan its endowment.

The quantity version of this proposition requires the strong assumptionthat factor prices are internationally equalized. Accordingly, Keuschnigg proposes avalue version combined with Cobb-Douglas technology. This approach has the


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property that factor cost shares can be treated as parameters. Further, Keuschnigggeneralises the basic value version of the HOV model to incorporate non-neutraltechnological differences, increasing returns (external and internal), mobile capitaland Armington demands. The relationship between the factor content of trade andfactor endowments survives the generalisation of the HOV model in this mannerand the remainder of the book is devoted to developing and implementing tests ofthe more general model.

The empirical work reported in the book reflects a data base of 46 countriesand 116 (108) manufacturing industries in 1989 (1979). Three factors of productionare postulated – capital, low-skilled labour and high skilled labour. The countriesinclude both developed and developing countries. Keuschnigg conducts a variety ofboth direct and indirect tests and concludes that ‘the analysis provides a reasonablygood explanation of trade data in terms of a very short list of production factors ….international trade, factor intensities, and factor endowments are related aspredicted by the HOV theory.’ (p 139). Thus, developing countries tend to ‘export’low-skilled labour and developed ones high-skilled labour. The results for capitalare not as clear cut and Keuschnigg reports that generally the theory fares better fordeveloping country trade than it does for developed countries.

Interesting subsidiary results include: i) when factor intensities are definedas relative cost shares no factor intensity reversals are observed; and ii) the perfectcompetition version of the HOV model cannot be rejected in favour of one whichincorporates scale economies and imperfect competition.

Sometimes in the transition economies questions of the following formhave been asked ‘in what does (say Latvia) have a comparative advantage?’ Thisvolume comprehensively demonstrates at least two things. Firstly, even to begin toaddress such a question needs an approach that goes well beyond the usual textbookversion of HO and Keuschnigg provides a solid account of the necessary theoreticalframework. So the book would be rather useful for a graduate student working inthis area. Secondly, the book suggests that the answer to such a question is likely tobe rather unsurprising.

Finally, a personal comment on popular misconceptions of the meaningand usefulness of the principle of comparative advantage. From time to time it issuggested that comparative advantage has a normative content, that comparativeadvantage should be identified so as to identify the sectors a country shouldspecialise in. This is quite the wrong way of interpreting the idea. Specialisationaccording to comparative advantage is simply the outcome of market forces whentechnology, markets etc satisfy certain restrictions. The merit of this book is toinform us a little more about the exact scope of those restrictions.

ALF VANAGS BICEPS

Call for Papers

TAX POLICY IN EU CANDIDATE COUNTRIES

ON THE EVE OF ENLARGEMENT

12-14 September 2003 - Riga, Latvia

Keynote Speakers: Gibert Metcalf, Tufts University – "Indirect Taxation"Helmuth Cremer, University of Toulouse – "Optimal Tax Policy"

Peter Lambert, York University – "Taxation and Equity"

TopicsEU tax harmonisation

Tax competitionTax administration

Local taxationEfficiency and equity of taxation

Tax evasionEU Accession

Tax abatementTax policy in the era of globalisation

Taxation in cyberspace

ScheduleSubmitted papers and abstracts (up to 200 words) must include title, keywords, JELclassification, full name, affiliation, address, email, fax and telephone of the author/s.Only email submissions will be accepted. Abstracts should be submitted by March 31st2003. Acceptance or rejection will be notified by April 30th 2003. Accepted final papersshould be received before June 30th 2003.

Participation fees (EUR 100) include conference papers, welcome reception andrefreshments.

Papers and abstracts should be sent to:Dr. Mark Chandler

EuroFaculty, University of VilniusSauletekio al. 9

Bldg. 2, Room 702LT-2040 Vilnius, Lithuania

[email protected]: www.eurofaculty.lv/taxconference

Baltic Journal of Economics - BICEPS

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