Baltic Journal of Economics

The Baltic Journal of Economics Jorgen Drud Hansen Editor in Chief University of Southern Denmark and EuroFaculty, University of Vilnius Raul Eamets Editor University of Tartu Mihails Hazans Editor University of Latvia Daunis Auers Managing 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

Foreword Gustav Kristensen Articles

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Some Unpleasant (and then Some Pleasant) Transition Arithmetic Robert Elder

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Financing Constraints as Determinants of the Investment Behaviour of Estonian Firms Jaan Masso

31

Gender Wage Differences in Soviet and Transitional Estonia Charles Kroncke and Kenneth Smith

50

The Outward Expansion of the Largest Baltic Corporations – Survey Results Kari 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

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

FOREWORD The first volume of the Baltic Journal of Economics (BJE) was published by EuroFaculty in 1997, the second volume following in winter 1999. There then followed a lull in activity. However, the growing need for a prestigious Baltic-based refereed economics journal called for the renewal of the organization of the Baltic Journal of Economics. A high technical standard, stable financing, and stable professional leadership has been secured through EuroFaculty Baltic resources and the generous support of our sponsors. The Baltic scientific capacity in the field of economics is currently quite small. This is the strongest argument for cooperation around one common scientific journal of economics for all three Baltic states. It is the goal of the BJE to raise the Baltic states scientific capacity in economics, over a number of years, to the level of comparably sized European countries e.g. Denmark, Sweden and the Netherlands Thus the function of the Baltic Journal of Economics is threefold: (i) to encourage Baltic scientists in their economics research by giving them a potential medium for refereed publication; (ii) to create a network of national and international referees who, through interaction with Baltic researchers, will increase the scientific level in the Baltic states; and (iii) to disseminate economics research on the Baltic States through the distribution of free copies of the BJE to academic and research institutions globally and the creation of an open web-site featuring all past and future editions of the journal.

Gustav Kristensen Director EuroFaculty-Tartu-Riga-Vilnius

1

Some Unpleasant (and then Some Pleasant) Transition Arithmetic

Some Unpleasant (and then Some Pleasant) Transition Arithmetic Robert 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 pay attention to what Monetarists say, and I cheerfully acknowledge that these two Monetarists thought up a clever title for the paper they wrote for the Minneapolis Fed’s Quarterly Review back in 1981. Here in 2002, the subject I address in the paragraphs below is not Monetarism, but instead the experience of transition economies. In particular, I call attention to some arithmetic that can help organize our thoughts with regard to some of the hardships a country endures and some of the successes a country achieves during the transition from central planning to free markets. I focus on equations involving important stocks and flows of people in such a country in transition, subsequently citing data on those stocks and flows between the years of 1992 and 2000 here in Latvia. When analyzing an economy in transition, a good place to start is with an equation describing the division of employment (E) between the state sector (ES) and the private sector (EP). E = E S + EP Each of the three magnitudes in this equation is a stock. At any moment in time, for example, we can observe the quantity of people with jobs (the stock of employment E), the quantity of people working for the state (the stock of state sector employment ES), and the quantity of people working for private firms (the stock of private sector employment EP). This equation can also be termed static, since it describes the state of employment at any given moment in time. If we move forward from one moment in time to some subsequent moment in time, we can make the equation 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 that runs 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]). This essay 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.

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Baltic Journal of Economics Autumn/Winter 2002

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 of employment 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 that ΔEP = – ΔES, so from the flow equation above ΔE = 0, and there would be no change in aggregate employment. In this event, the sectoral reallocation of labor associated with transition might appear relatively painless. But transition is not that easy, and indeed can be painful, because the outflow from state sector employment does not necessarily 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 into private sector employment but also to a flow into unemployment. To bring unemployment into the arithmetic, we begin once more with an appropriate stock equation. At any given moment in time, the economically active segment of the population (EA) consists of those who have jobs (in the state sector, ES, and in the private 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 associated dynamic expression would be ΔEA = ΔES + ΔEP + ΔU. From this flow equation, we can verify the fact that for an economically active population 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 flow into unemployment (ΔU > 0). But unfortunately, the unpleasantness of state sector shrinkage does not stop with rising unemployment. To see why, observe first that the 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 and flow equations. Starting once more with the pertinent stocks, note that we can dichotomize the entire population (POP) between the economically active (jobholders, 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 employment (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 apparent when any such flow involves involuntary retirement. And finally, when we

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acknowledge that the assumption of a constant population (ΔPOP = 0) is inappropriate to transition scenarios, we should add that a flow out of state sector employment can imply (4) a flow out of the population (ΔPOP < 0). From the somewhat unpleasant to the definitely unpleasant, such flows out of the population can range from the temporary life disruptions of emigration to the permanent life terminations of death. As shown by the data below, emigration in excess of immigration and deaths in excess of births have been a steady feature of the transition years from 1992 through 2000 in Latvia2. As it chronicles these nine consecutive years of population decrease, notice that the table reveals net emigration as the larger source of population decrease during the first three years and 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 equation in 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 redeclaration

of Latvia’s independence in May 1990 through the present. I focus on 1992 through 2000 because these are the years for which there exists available data for each of the variables that I have introduced in the preceding discussion.

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population implied by Table 1. Also readily apparent from this table of stock magnitudes are two distinguishing features of transition: monotonically decreasing state sector employment and monotonically increasing private sector employment. In contrast, as shown by the final two columns of data, the stocks of the unemployed and the economically inactive display non-monotonic behavior, decreasing during some years and increasing in others. To organize our thoughts about the behavior of the five stocks recorded in Table 2 and to examine the transition from a different angle, I use the stock data provided by Latvia’s Central Statistical Bureau to construct the table pertinent to our final flow equation. The results appear in Table 3. Since each stock in Table 2 is an annual average, each flow in Table 3 is the difference between each pair of consecutive annual averages, as indicated 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 that we 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 equation groups outflows on the left-hand side and inflows on the right-hand side. During these years, this equation says that the sum of outflows from state sector employment, the economically inactive, and the population were equal to the sum of inflows into private sector employment and unemployment. For example, from 1992 to 1993 the observation ΔES = – 170 implies – ΔES = 170, an outflow of 170,000 people from state sector employment available as a source of inflows into private sector employment and unemployment. Similarly, the 1992 to 1993 observation ΔEI = – 25 implies – ΔEI = 25, an outflow of 25,000 people coming from the ranks of the economically inactive to provide a second source of inflows into private sector employment and unemployment. In contrast, the 1992 to 1993 observation ΔPOP = – 52 remains negative on the left-hand side of this equation, since an outflow of 52,000 people from the population diminished what was available to facilitate inflows into private sector employment and unemployment between these years. To highlight the leading destinations of workers leaving the state 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, unemployment absorbed the largest share of shrinking state sector employment from 1993 to 1994, and the population absorbed the largest share of shrinking state sector employment in 1994 to 1996. Following 1996 to 1997, which was unique for its flow into economic inactivity, we move to the final three years from 1997-2000, during which the outflows-equalsinflows equation becomes – ΔES – ΔEI + ΔPOP – ΔU = ΔEP. For each of these last three years, this equation features four outflow terms on the left and one inflow term on the right. Happily, note that the only stock absorbing inflows during these last three years is private sector employment. Although outflows from the population reduce what could have been additional inflows into private sector employment, positive contributions to these recent years of inflows into private sector employment are made by outflows from state sector employment as 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 of economic transition is sprawling, and there are many aspects of it left unaddressed in this brief note. Further, there are many countries involved in economic transition, and a comparison between their experiences and those of Latvia would provide additional perspective. As we have seen here in Latvia, the early years of transition can feature some unpleasant dynamics, but as we have also seen more recently, 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 AS DETERMINANTS OF THE INVESTMENT BEHAVIOUR OF ESTONIAN FIRMS1 Jaan Masso2

Abstract Lack of financing is arguably the main obstacle for making profitable investments in transition economies. In this paper we investigate whether there is underinvestment due to financing constraints in Estonian manufacturing firms. Firm level panel data from 1995 through 1998 with several items from financial statements were used. The unique data includes many very small firms with assets less than 1 million USD that are often not explored in empirical studies. The existence of liquidity constraints was tested with estimating regression coefficients of inside - firm financing from reduced form investment regressions and using the investment Euler equation. Results show that internal finance played a bigger role for investments made by small firms and firms owned by domestic (non-foreign) capital. The only exception is that in the Euler equation, cash flow influenced investments more for small than for large foreign firms. The results imply that foreign direct investments and lower corporate income taxes can promote investments through the relaxation of liquidity constraints. JEL classification: G31, E22 Keywords: Financing constraints; Investment; Cash flow; Estonia 1 Introduction Since the seminal article by Fazzari et al. (1988) numerous papers discuss the effects of financing conditions on the investment decisions of private firms. In several papers, it has been found that investment is more sensitive to the availability of internal funds among certain groups of firms that are more subject to the presence of information and agency problems in financial markets. These include, among others, small and young firms, firms without credit ratings, and firms without 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, three

anonymous referees, the editor and participants of seminars held in Stockholm, Tartu and Tallinn that have significantly contributed to the quality of the paper. I am also obliged to Urmas Varblane for generously providing the data that was used in this study. All remaining errors are of course my sole responsibility. 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) has several implications for tax policy, corporate take-overs and the channels of macroeconomic policy (Hubbard, 1998). There is a widespread view among economists that capital market imperfections are especially severe in Central and Eastern European transition economies (Coricelli, 1996). This is because many firms in the new market economies 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 to lend long-term (Pissarides, 1998). On the demand side, firms need to invest heavily in order to modernize obsolete capital stock and increase competitiveness in world markets. Thus, the lack of financing probably constitutes one of the main obstacles to 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 shown that investment in firms belonging to financial-industrial groups in Russia is less sensitive 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 internal finance and gross investment, although in their later study (2000) of net investment retained profits were shown to have positive effect. Anderson and Kegels (1997) also found evidence of the influence of financial variables like cash flow, beginning-ofperiod bank debt and trade credit on the fixed investment of Czech enterprises. Bratkowski et al. (1999) argue that imperfections in capital markets in Central European economies do not seem to affect the growth of new private firms. For Bulgaria, Budina et al. (2000) found liquidity constraints to be important for small firms but not for large. This finding was explained by the inefficiency of the financial sector because of loans granted to large unprofitable firms. One weakness of these studies is that their inferences have been based on reduced form investment regressions, rather than explicit conditions of optimal capital accumulation. In the present paper, we try to see whether firm size and ownership are important in determining whether Estonian industrial firms can finance profitable investment projects. We succeeded in getting access to enterprise level panel data that allows an examination of how the severity of financing constraints varies across different types of firms. Also, in this way the aggregation bias can be avoided. Thus we focus only on fixed investments; however financing constraints could influence also 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 the investment Euler equation derived from the objective of maximizing a firm’s value under convex adjustment costs and constant returns to scale. The results from both approaches show that the availability of internal finance plays a significantly bigger role for investments of small and domestically owned private companies. The policy implication of the results is that recent changes in taxation law in Estonia that have made taxation more favourable for retained earnings than for dividends, promote investment of the aforementioned firms. Also, the range of benefits for attracting foreign direct investments into the country can be seen more widely - alongside other positive effects foreign direct investments could also loosen liquidity constraints. The remainder of the paper is organized as follows. Section 2 provides some stylized evidence of investment and financing behaviour of Estonian firms. Section 3 describes the dataset of Estonian manufacturing firms. Section 4 describes the method and results of the reduced form investment equations and section 5 those of the more elaborate Euler equation. The final section concludes with discussion of the findings and policy implications. 2 Stylized facts of the investment and financing problems of Estonian firms Before a formal statistical analyses we will present some stylized evidence about the investment and financing behaviour of Estonian firms. First, the Estonian Institute of Economic Research has conducted inquiries among Estonian firms about their investment decisions (Konjunktuur, 1999). Table 1 summarizes the relevance of different factors limiting investment. As we can see, in all years the biggest obstacle for investment has been a small profit. On the one hand this could simply reflect a low internal rate of return in comparison to the cost of capital, as noted by Raudsepp and Leoshko (1999). But on the other hand this could also show a tendency to mostly rely on internal funds when carrying through investments. Many studies in developed economies show internal finance or cash flow to be the primary source of funds, e.g. Fazzari and Petersen (1993) found that cash flow constitutes 71 % of net sources of finance for US public firms paying dividends less than 10 % of earnings. For Estonia it has been argued that internal financing constitutes a smaller part of funds than in developed countries because of a lack of internal funds and unstable economic development. For instance, Kangur et al. (1999) estimated that about 49 % of total investment was internally 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 % noted this as a problem). For example, in a survey among firms in the United Kingdom only 6-10 % viewed the cost of finance as a limitation to capital expenditures (Bond and Jenkinson, 1996). This finding for Estonian firms could result from the cost disadvantage of external funds due to high transaction costs, agency costs and asymmetric information. Earlier papers on the financial problems of Estonian firms have found the cost of capital to be too high, especially for small and medium size enterprises (Raudsepp and Leoshko, 1999). Difficulties in obtaining credit (19-31 % of respondents) could possibly reflect credit rationing, i.e. some applicants are denied loans in spite of their readiness to carry all the price and non-price components of the loan contract (see Stiglitz and Weiss, 1981) or difficulties with necessary collateral, both of which are consistent with an imperfect capital market story. Similarly, in the aforementioned study of UK firms only 2-3 % of firms reported an inability to raise external finance as a problem (Bond and Jenkinson, 1996). We could infer that financial factors seem to constrain capital investments more in transition economies than in developed western economies. In particular the availability and price of external (new debt and equity) versus internal financing (internally generated cash flow) is an issue. According to the financing hierarchy hypothesis firms prefer to use internal financing due to asymmetric information between managers and potential new equity investors or creditors; external funds are only used after internal sources are exhausted (Fazzari et al., 1988). One survey among Estonian non-financial firms listed in the Tallinn Stock Exchange showed the presence of a financing hierarchy – internal equity was ranked as the most preferred source of financing (Raudsepp et al., 2000). Low dividend payout rates (on average 10 %) confirm this finding (Ibid.) because the cost disadvantage of external 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 4 times smaller than in European firms (Pärn and Lumiste, 2000). This may have some negative effect on the amount of internal finance available due to a smaller tax shield of depreciation. The Bank of Estonia carried out a study on the financing of the Estonian entrepreneurial sector during 1994-1998 as well as connections between financing and investment (see Kangur et al., 1999). The authors argued that aggregate data shows co-movement between external financing and total investment (investments in fixed assets plus), so that (external) financing can be an important factor affecting the level of investments. 3 Data and summary statistics In the present study we used firm-level financial statements panel data collected and compiled by the Statistical Office of Estonia. The original dataset includes 373 industrial enterprises for the period 1995-1998. Only firms in manufacturing industries were considered. Here manufacturing firms are those with 2-digit EMTAK codes between 15 and 39 that correspond to section "D" of European Union NACE 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 manufacturing industry. 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 rather than small firms. When we consider that financing constraints can prevent business from starting (see Evans and Jovanovic, 1987), so that some survivorship bias is introduced, it can be suggested that the present study will tend to underestimate rather than overestimate the importance of financing constraints. Several firms were deleted from the sample. First, all firms with negative or zero fixed tangible assets were deleted. Second, the possible effect of outliers on regression estimates was controlled by excluding firms with observations of sales growth, 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. The justification for excluding firms with extreme growth rates in sales or investment is that 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 bias the estimates of investment-cash flow sensitivities towards one, particularly in firms with higher annual growth rates (Kaplan and Zingales, 1997). Table 2 presents summary statistics for some of the regression variables as well as the relative importance of different sources of finance for different sub samples of firms (the three last rows of the table). First, the total sample was split into 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, so the need for extra financing is greater. As expected, cash flow plays a bigger role as a source of financing for smaller firms. Both cash flow and investments are more volatile for smaller firms. In earlier studies other researchers have found the same evidence for classes of a-priori constrained firms (Fazzari et al., 1987). Only for the 3rd group is new equity an important source of funds. In total (last column of table 2) firms have been investing quite actively (average investment to capital ratio 0.40). This has been largely financed by cash flow. Still the relative importance of cash 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 sources ratio to be 0.715. We can also see that firms belonging to foreign capital are on average much bigger in terms of total assets and capital, and grow faster. The first evidence can be explained by the fact that Estonian residents do not have enough capital (neither could they borrow the funds) to privatize large state-owned firms. Both investments and cash flow are more volatile for domestic firms. Foreign firms also got remarkably more new equity capital, which indicates their better access to external financing. Here the firm is defined as belonging to foreign or Estonian capital if in all years (1995-98) more than 50 % of the share capital belonged to foreign or Estonian 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 reduced form investment equations The existence of liquidity constraints is usually tested by regressing the investment on variables that measure the availability of financing generated inside the firm and some 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 q that theoretically should capture all relevant information and is basically the ratio of 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, so we are unable to calculate such a measure. Instead we use employment growth to control 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 the collateralizeble value of illiquid assets), while they also convey information about what proportion of investment spending can be internally financed (Schiantarelli, 1996). Firms with a higher level of liquidity can better collateralize debt issues and receive loans at lower interest rates as well as exploit more relatively cheap internal funds. It means that we are testing whether internal and external financing are perfect substitutes or not. The expected impact of cash flow and cash stock on investment is positive. The intuition for including the leverage variable is that agency costs occurring due to diverging interest of lenders and borrowers (e.g. monitoring and bankruptcy costs) are assumed to increase in the amount of debt used. A higher level of debt in the beginning of the period makes it more difficult to finance new investment projects, if there is a limit to the debt a firm can have. So we estimated the following equation:

(4.1) where I denotes gross investment, LGROWTH employment growth measured in logarithms, CF cash flow, CS cash stock, K capital stock and DEBT/A is the ratio of short- and long-term debt to total assets.4 The intercept coefficients, γi and γt allow for firm specific and year intercepts; uit is random error term. Firm dummies γi control for the effect of variables that are constant over time but are excluded from the model (e.g. industry classification of firm). Hereby investment is measured as change in fixed tangible assets plus depreciation; cash flow is the sum of net income and depreciation. All variables (except debt and employment growth) are normalized 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 suggested

by 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 transition countries 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 proportional to 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 measured as the net value of fixed tangible assets. The stock variables are measured at the end of 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 as evidence of financing constraints is that cash flow might actually proxy for the profitability of new investment projects. Since Fazzari et al. (1988) the strategy has been to split the sample by some criteria associated with problems of raising funds on the credit and capital markets and compare the relevance of inside firm liquidity between different sub-groups. Plausible criteria include inter alia firm size, firm age, the existence of close relationships with industrial or financial groups, the presence of credit rating or commercial paper programs, dividend policy etc. If for the class that is a-priori classified as financially constrained, the cash-flow sensitivity is significantly bigger and statistically more significant, then this is interpreted as evidence of the presence of financing constraints, assuming that profits have the same relevance as measure of profitability of new investment for different firms. We split the sample along two lines. First we use firm size as a proxy for the ability to raise funds through external financing. The rationale is that firm size could be a proxy for firm age and other unobservable firm attributes that affect the degree to which public information about the firms’ investment projects is available. Small firms probably include many newly created de-novo firms, which lack credit history and collateral. It is also plausible that the transaction costs of obtaining funds contain a significant fixed cost component. The presence of such increasing returns suggests that the cost of obtaining external funds are higher for small than for large firms.5 It has also been emphasised in earlier studies that in transition economies the 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 the sample period. Real assets were calculated with GDP deflator. One possible criticism to the usage of firm size as a criterion of whether particular firm is liquidity constrained or not, is that the costs of financing could decline with size due to a lower risk for the bank, not necessarily due to smaller information problems.6 Smaller firms in particular usually have a lower survival probability than large firms (Audretsch et al., 1999) and banks’ loan losses are found 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 loans than just a few big loans, because in the former case, due to the law of large numbers, 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 gross proceeds 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 large ones due to a better spread of claims over time. Secondly, if firms’ owners and banks had exactly the same information about project risk, then the required rate of return from the risky project is probably higher anyway, so the owners are less willing to finance these projects. The source of liquidity constraints (or that firms internal funds and profits are correlated) is the asymmetric information concerning projects returns, not just the possibility of the failure of the project. After investigating the effect of firm size on investment-cash flow sensitivity, we tried to see whether there are any differences in the investment behaviour of firms owned by foreign capital versus those belonging to private domestic capital. The enterprises of the first group are at least partly subsidiaries of foreign parent companies (as argued by Kangur et al., 1999). So they could receive funds from the internal capital market of the international corporation, (as first studied by Hoshi et al., 1991), as well as receive cheaper and longer-term credits from foreign credit markets. We defined firms as belonging to foreign or Estonian private capital if in all years of the sample period (1995-98) more than 50 % of the share capital belonged to foreign owners or Estonian private capital. In both classifications firms are not allowed to change their group affiliation, although in a rapidly developing economy this may be inadequate: small firms grow, their net worth increases, and more information on them becomes available, so firms’ financial constraint status may change. Next we report results of estimating equations (4.1) for different sample splits. As stated, ‘fixed-effects’ or ‘within-groups’ estimators were used. This means that the deviations of variables from their firm-means were used in regressions. As the regression equation was not derived explicitly from any structural model, the parameters should be interpreted as partial correlation coefficients rather than estimates of structural coefficients. First, the results for different size groups (see table 3 below) indicate that the coefficients of both measures of internal liquidity (cash flow and cash stock) decrease monotonically with firm size. The same applies to the statistical significance of the parameters. This is evidence in favour of the hypothesis that large firms can more easily finance their investments and face less severe financing constraints. It is important to emphasize that because cash flow may actually proxy for the firms' investment demand, it is the difference in the estimated values of parameters that matters rather than just the size of the individual parameters. The t-statistic under the null hypothesis that small and medium size firms have the same cash flow coefficient is 2.54. The t-statistic under the null hypothesis that large and medium sized firms have the same cash flow coefficient is 2.34. This means that the difference is also statistically significant. Coefficients of leverage variable are negative for small and medium sized enterprises, but insignificant for large firms. It suggests that strength of balance

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sheet is more important for smaller firms. Parameters of the employment growth variable are significant in two out of three regressions, so hopefully we have been able to control for the existence of investment opportunities at least partially. Adjusted R2-s in investment equations are similar to the ones observed in other studies. 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 are presented. Let us first note that foreign firms tend to be much larger than domestic in terms of average value of assets (see table 2). In order to control for the firm-size effect we split the sample of domestic corporations ordered by the period's average real assets into 3 groups (48 firms each): small, medium and large enterprises. Similarly, the sample of foreign corporations was split into two groups (15 firms each). The foreign firms were divided into two groups due to the much smaller number of foreign firms in our database. As we can see from table 4, both cash flow and stock have a strong positive effect on investment for different groups of Estonian firms (except the cash stock for medium sized firms). In comparison to domestic firms, the coefficients are much smaller and less significant for both large and small foreign corporations. This finding was also robust to other specifications of the model not reported here (for example, when investment was regressed only on cash flow and cash stock etc.). It is interesting that the cash flow parameter for small foreign firms is smaller than that of large Estonian firms although the firms in the second group are about four times larger in terms of total assets (respectively 1.09 and 5.13 millions of USD). If only firm size affected cash flow – investments relationship, then the cash-flow parameter would be bigger among large Estonian firms, not among small foreign firms. The medium Estonian firms are almost of equal average size (1.05 million USD) to small foreign firms, but the cash flow parameter is about 60 % bigger in that group. We can conclude that affiliation to foreign capital significantly loosens financing constraints, increases investment and thereby supports firm growth. On the other hand the results should be treated with

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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 capital investments since firms smooth investment with working capital to maintain desired investment levels. So we also estimated the investment regressions that were augmented with the working capital investment variable. In order to account for the endogeneity of working capital investment, two-stage least squares estimation was used whereby the working capital investment was instrumented with 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 increased significantly in size but the pattern across size and ownership classes remained the same. The sign of the working-capital investment variable after inclusion in the left side of regression (4.1) turned out to be negative. According to Fazzari and Petersen (1993) the last outcome should address the criticism that positive correlation between investment and cash flow arises because cash flow proxies for investment demand. The intuition is that if it is less costly to decrease working capital investments than fixed investments, liquidity constrained firms should in the periods of temporary cash flow shortfall decrease rather investments in working capital (up to drawing these to negative levels) than in fixed assets that generates the negative relationship between the two kinds of investments. The other possible way to modify the model concerns how far the variation of parameters is tested. Instead of dividing firms into sub-groups and then estimating the same equation separately for each group one could also use the expansion 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 when testing for liquidity constraints instead of grouping firms into sub-samples and then estimating the equation 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 the foreign capital and SIZE is a measure of firm size defined as the natural log of the average 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, as it 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 the differences due to size and due to ownership in one model. Alternatively, one may argue that in the first model some variables are omitted, which we expect to be of importance (size, type of owners), and hence we would expect biased estimates. The estimation results are presented hereby in the table 5. As the reader may see, the qualitative results still hold: both cash flow and cash stock have significant positive effect on investments (as shown by the positive values of parameters δ21 and δ31). For the domestic firm with average size (FOR=0 and SIZE=log(16 000 EEK)=9.68) 1 kroon increase in cash flow increases investments by 0.522 kroons (i.e. the value of parameter δ21 plus 9.68 times the value of δ22). The positive effect of liquidity declines both with firm size (due to the negative value for parameter δ22) and is smaller for foreign owned firms (negative δ23). The impact of the leverage or indebtedness variable on investments is still negative, but diminishes with the firm size (negative δ41 and positive δ42). Finally it seems not to matter much for the results whether the effect of liquidity is assumed to change with firm size continuously (like here) or discretely (results in tables 3 and 4).

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

5 Test of liquidity constraints with Euler equation In order to strengthen the robustness of the above results we used the so-called Euler equation approach. The Euler equation is a relation between investment in successive periods derived from a dynamic maximization problem. Thereby the impact of expectations and profitability on investments are considered in a more plausible manner than simply estimating ad-hoc regressions. We used the model that has been derived and estimated by Bond and Meghir (1994). The later modifications 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 presented here. The derivation of the model starts with the assumption that the firm’s objective is to maximize its net present value. In the absence of taxes it is given at the start of period t as (5.1) In the equation above Πτ( ) stands for firms net revenues or profits, Kt for capital, Itfor investment, Lt for variable factors and βtτ+1 for discount factor between periods t and t+1 that derives from nominal required rate of return rt as βττ+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)Kt-1+It, where δ is the rate of economic depreciation. As usual, quadratic linearly homogenous function is assumed for adjustment costs (i.e. constant returns to scale

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with respect to capital and investment):

(5.3) Parameter b measures the size of adjustment costs; parameter c indicates the optimal investment/capital ratio based on the adjustment costs. The judgement for the quadratic form is based on the fact that higher deviations from the equilibrium are more costly than just an oscillation around the optimal level (Lizal, 1998). Differently, due to convex adjustment costs firms should try to divide the desired investment over several consecutive periods instead of making all the investment in one 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 ptI denotes the price of investment goods. In order to allow for imperfect competition, pt is let to depend on output, with constant price elasticity of demand (ε >1). The optimal investment path can be described in terms of an Euler equation that under the aforementioned assumptions derives as follows (the complete derivation can be found at Bond and Meghir 1994): (5.5) where Jit denotes user cost of capital. So, in the Euler equation (that relates marginal adjustment costs in adjacent periods), current investment is positively related to expected investment and to the current average profits (reflecting the marginal profitability of capital under constant returns), and negatively related to the user cost of capital.8 An attractive feature of the Euler equation model is that all relevant expectational influences are captured by one-step-ahead investment forecast (Bond et al., 1997). So, it should control for the usual criticism to other types of models, that financial variables do not capture the effect of liquidity constraints, but rather expectations of future profitability.

When expectations are replaced with realizations and parameters in the resulting regression equation are assumed to be constant over time, then the empirical 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 is calculated as follows. First, the sum of opportunity cost of funds and depreciation minus expected appreciation of capital goods is calculated. Then the result is multiplied with the relative price of capital goods. See also equation (5.7).

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(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 and the term Yit =Fit - Gitis output net of adjustment costs. The regression variables are calculated from empirical data as follows. First, like previously investment (Iit) is measured as a change in fixed tangible assets plus depreciation. Output Yit is the sum of sales revenues plus change in finished goods inventories. Cash flow Cit is here defined as operating profits before taxes and interest plus depreciation of fixed assets9 . 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 index was used for pt and the deflator of gross capital formation expenditures for ptI. The Statistical Office of Estonia publishes the data on both price indices. Required rate of returnrt was proxied with long-term interest rate on bank loans nominated in Estonian kroons in the beginning of the period. The Bank of Estonia publishes the data about the interest rates. Notice that quite often the user cost of capital term is eliminated from the model altogether and replaced with fixed time and firm effects (see e.g. Bond, Meghir, 1994). Following Whited (1992) the rate of economic depreciationδi is calculated as δ= 2/L, where L denotes the estimated average life of capital goods, and Lt is calculated as Lt=(GKt-1+It)/DEPRt, where GKt-1 is the reported value 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 imperfect competition or non-constant returns to scale. Lagged investment terms consider the effect of adjustment costs. By estimating the equation we are controlling for the relation between current profits and expected future profitability (Gaston and Gelos, 1999). Under the null hypothesis of no financial constraints, the parameters should satisfy conditions β1 ≥1, β2 ≤1 and β4