This Selected Issues paper focuses on financing constraints and productivity in Estonia. The paper examines two questions: (1) is there evidence of financing constraints among Estonian firms; and (2) have financing constraints reduced firm-level total factor productivity (TFP)? These questions are particularly important in the current environment, in which credit growth in Estonia has slowed down considerably and firms may face increasing financing constraints that could dampen growth. The paper also provides empirical evidence on the existence of financing constraints among Estonian firms.

Abstract

This Selected Issues paper focuses on financing constraints and productivity in Estonia. The paper examines two questions: (1) is there evidence of financing constraints among Estonian firms; and (2) have financing constraints reduced firm-level total factor productivity (TFP)? These questions are particularly important in the current environment, in which credit growth in Estonia has slowed down considerably and firms may face increasing financing constraints that could dampen growth. The paper also provides empirical evidence on the existence of financing constraints among Estonian firms.

I. Financing Constraints and Productivity in Estonia1

A. Introduction

1. Economic convergence in Estonia has been accompanied by financial deepening and a fast expansion of private sector credit (Figure 1). Since 2000, real private sector credit has increased by an average of 28½ percent a year. As a result, the private sector credit-to-GDP ratio, at 103 percent in 2007, is only slightly below the average for the euro area. This rapid expansion reflects a variety of factors, including initially low levels of financial development; pent-up demand pressures following decades of socialist management; good macroeconomic policies and accession to the European Union (EU), which have helped lower the risk premium; and improved access to foreign capital through the entry of foreign banks.

Figure 1.
Figure 1.

Contribution to Private Sector Credit Growth, 2000:Q4-2008:Q1

(Year-on-year percent change)

Citation: IMF Staff Country Reports 2009, 085; 10.5089/9781451812541.002.A001

Source: Eesti Pank.

2. Yet, there is some evidence that insufficient financing may have limited growth prospects. First, credit growth has not been evenly distributed across sectors. In particular, financial intermediation and real estate have been the main drivers of credit growth with other sectors playing a much smaller role (Figure 2). Therefore, credit allocation may have resulted in limited access to finance for some firms. Second, more than 60 percent of corporate investment in Estonia is financed with internal funds.2 Finally, the 2006 progress report on the implementation of the Lisbon Strategy argues that Estonia’s adoption of new technologies is hindered by insufficient access to capital.3 These factors suggest that some firms may be constrained in their investment and input decisions, with a potentially detrimental impact on productivity relative to unconstrained firms.

Figure 2.
Figure 2.

Contributions to Corporate Credit Growth by Industry 1/

(Year-on-year, percent change)

Citation: IMF Staff Country Reports 2009, 085; 10.5089/9781451812541.002.A001

Source: Eesti Pank.1/ Excluding leasing.

3. This paper examines two questions: (1) is there evidence of financing constraints among Estonian firms? and (2) have financing constraints reduced firm-level total factor productivity (TFP)? These questions are particularly important in the current environment, in which credit growth in Estonia has slowed down considerably and firms may face increasing financing constraints that could dampen growth.

4. The rest of the paper is organized as follows. Section B describes the data. Section C provides empirical evidence on the existence of financing constraints among Estonian firms. Section D explores the question of whether financing constraints may have decreased firm-level productivity, and Section E concludes.

B. Data

5. Our data come from the Estonian Business Registry and cover the period 1997–2005.4 The data set is an unbalanced panel containing detailed information on balance sheets and income statements of all registered firms in Estonia. However, primarily due to missing information, but also the exclusion of extreme or unrealistic observations, only the data of 45 percent of the firms in the registry (up to 19,000 firms) can be used. One of the unique features of the data set is the absence of any size thresholds: 69 percent of the firms are microenterprises. In addition, more than 90 percent of the firms are privately owned. This makes this data set particularly well-suited to analyze the implications of financial frictions since privately owned firms, and small and medium-sized enterprises (SMEs) in particular, usually receive a very small share of credit in many emerging markets, despite accounting for a large share of enterprises, employment, and output. Another salient feature of the data set is the availability of data from all economic sectors in Estonia.

6. A preliminary analysis suggests there are sharp differences between firms with external financing and those without. Table 1 shows more than half of the firms in our sample have no long-term liabilities on their balance sheets during their entire life span. These firms are on average much smaller in terms of number of employees, sales, or value added, and they are slightly younger. In addition, their capital intensity and investment rates are considerably lower than those of firms that borrow from banks or private investors. Firms without debt are also on average less productive. This difference is rather small until the year 2003, but no-debt firms’ productivity increased only slightly thereafter, whereas firms with debt experienced an exponential growth in their labor productivity (Figure 3).

Table 1.

Summary Statistics

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Sources: Estonian Business Registry database; and authors' calculations.

Notes: Firms are divided in two groups: firms with long-term liabilities (Debt) and firms with no long-term liabilities on their balance sheets during their entire lifespan (No debt). All variables are measured in thousand of Estonian Krooni and deflated by two-digit sector deflators; all variable definitions can be found in Appendix I. Capital intensity is defined as (net real tangible + net real intangible assets-goodwill)/labor; labor productivity = real sales per worker; investment ratio = real investment/real total assets lagged one year.

Figure 3.
Figure 3.

Labor Productivity

(Sales per worker, thousands of Estonian krooni)

Citation: IMF Staff Country Reports 2009, 085; 10.5089/9781451812541.002.A001

Sources: Estonian Business Registry database; and authors’ calculations.

C. Are There Signs of Financing Constraints?

7. To construct a measure of financing constraints, we build on the literature dealing with the sensitivity of investment to internal finance. The basic premise is that asymmetric information and incentive problems make external financing more expensive than internal financing.5 As a result, firms with weak balance sheets may have limited access to external finance, and are obliged to rely on internally generated cash to finance their investment projects. The majority of the empirical literature has interpreted the excess sensitivity of a firm’s investment spending to its ability to internally generate cash as evidence of financial constraints.6 Building on this literature, we estimate the following investment model:7

(IitKit1)=θ0+θ1(Iit1Kit2)+θ2+(SalesitKit1)+(CashitKit1×Ωit)+αi+δt+εit,(1)

with

Ωit=δ1I1++δNIN+λ11n(size)it+λ2(Age)it+λ3(Leverage)it+λ4Foreignit,(2)

and where Iit is the investment expenditure of firm i at time t; Salesit is the net revenue received from the sale of products, goods, and services; Cashit represents a firm’s internal financial position at the start of period t; αi represents a firm fixed effect; δt denotes a time dummy; Sizeit is measured as total assets at the beginning of period t; Ageit is the age of the firm at the beginning of period t; Leverageit stands for the ratio of long-term liabilities to total assets at the beginning of period t; Foreignit is a dummy equaling 1 if more than 50 percent of the shares is foreign owned at time t; and (I1,…, IN) are industry dummies. The estimated coefficients for the δ’s and the λ’s in equation (2) are then used to calculate a firm-specific score of financing constraints F^itn based on the firm’s characteristics:

F^itn=δ^nIn+λ^1Sizeit+λ^2Ageit+λ^3Leverageit+λ^4Foreignit.(3)

The bigger the F^itn, the higher the degree of financing constraint. Although the coefficients are constant over the entire sample period, the characteristics of each firm change over time, and, hence, the degree of its financing constraint does also.8

8. Our results indicate that financing constraints vary across firms and sectors. In particular, young and highly indebted firms tend to be more financially constrained (Table 2). However, contrary to expectation, there are no significant differences in financing constraints based on firm size. Foreign firms, however, seem to have easier access to external capital. Overall, a large number of firms display some degree of financing constraint, and, at the sectoral level, the primary sector (“agriculture,” “mining and quarrying,” and “energy, gas, and water supply”) is the one with the highest constraint (Table 3). In particular, the score of financing constraint implies that, on average, a 1 unit increase in the ratio of cash to capital results in a 0.6 increase in the investment rate in the primary sector. By contrast, such an increase raises the investment rate by only 0.1 for most other sectors, except for wholesale and retail trade, where it increases the investment rate by a mere 0.03. However this does not imply that firms in these sectors are not financially constrained, particularly given the variation of the score across firms within a sector.9

Table 2.

Determinants of Financing Constraints 1/

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Sources: Estonian Business Registry database; and staff calculations.

The numbers reported are the estimated coefficients for the λ's in equation (2). Three dots indicate that the coefficient was not significant. For details on the estimation procedure, see Moreno Badia and Slootmaekers (2008). Significance level: *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 3.

Magnitude and Distribution of Financing Constraints by Sector 1/

article image
Sources: Estonian Business Registry database; and staff calculations.

Financing constraints are calculated based on equation (3).

9. For most sectors, financing constraints have remained roughly constant over time. In principle, we would have expected financing constraints to ease over time, as the degree of financial intermediation increased in Estonia during this period. However, the average of the score (F^itn) for most sectors has remained relatively constant for the entire sample period. The only exceptions are “agriculture” and “mining and quarrying” where the average score has actually increased (Figure 4). The relatively flat trend of the degree of financing constraint for most sectors could suggest that the demand for credit during this period increased more than the available funds because of the emergence of new financing needs with the growth of the economy and the entry of new firms. On the other hand, it could also be indicative of credit misallocation since some firms within particular sectors may have received the bulk of the credit.

Figure 4.
Figure 4.

Mean Financing Constraints by Industry, 1998-2005 1/

(Sales per worker, thousands of Estonian krooni)

Citation: IMF Staff Country Reports 2009, 085; 10.5089/9781451812541.002.A001

Sources: Estonian Business Registry database; and staff calculations.1/ Financing constraints are calculated based on equation (3). The average financing constraints for the “manufacturing” and “research and business activities” sectors are very close and, therefore, they overlap in the graph.

D. Have Financing Constraints Reduced Firm-Level TFP?

10. To analyze the relationship between financing constraints and productivity at the firm level, we develop a structural approach.10 In particular, we incorporate the score of financing constraints directly as a regressor in a production function equation while allowing productivity to evolve as a first-order autoregressive process:

yit=β0+β0lit+βkkit+βa1n(Age)it+βfF^it1+δj+δt+ωit+εit,(4)

where i and t indicate firm and time respectively, and all variables are presented in natural logarithms; yit is value added; lit is the labor input; and kit is the capital stock. The error term has two components: a productivity term, ωit, known to the firm and correlated with the inputs, and a random productivity shock, εit, with zero mean and uncorrelated with inputs, financing constraints, and firm characteristics. To estimate equation (4), we modify the Levinsohn-Petrin algorithm (see Levinsohn and Petrin (2003)) and treat financial constraints as an additional state variable.11

11. Surprisingly, financing constraints do not appear to affect productivity in most sectors. The estimated coefficient of financing constraints in equation (4) is not significantly different from zero in eight out of ten sectors (Table 4). Only in the sectors “construction” and “R&D and other business activities” is the coefficient negative and significant. However, this result is only robust to changes in the model specification for the “R&D and other business activities” sector, where the dampening effect of financing constraints on productivity is remarkably large.12 A possible explanation for this last result is that firms in “R&D and other business activities” need a continuous inflow of fresh capital to keep up with the latest technology and invest in frontier research. In general, this involves risky investment, and few banks and investors are willing to take that risk. Our results seem to suggest that even the smallest constraint in obtaining adequate funding has a large impact on the productivity of firms within this sector.13

Table 4.

Results for the Structural Approach, by Industry

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Sources: Estonian Business Registry database; and staff calculations.

Notes: Financing constraints are directly included in the TFP estimation as an additional state variable. The dependent variable of equation (4) is the log form of real value added, and we use bootstrapping methods (1,000 replications) to obtain correct standard errors (reported in brackets). The structural approach is estimated for each one-digit industry separately. R-squared statistics are not available for the modified Levinsohn-Petrin estimation. Though not reported, all regressions include two-digit industry dummies and time dummies. Significance level: ***p<0.01,**p<0.05,*p<0.1. Variable definitions are in Appendix I.

E. Conclusions

12. Although many Estonian firms appear to have faced financing constraints in recent years, this has not resulted in lower productivity. We find that the investment of both young and highly indebted firms is more sensitive to internal funds and, as expected, foreign firms tend to be less financially constrained than the average Estonian firm. Overall, a large number of firms display some degree of financing constraint, with firms in the primary sector the most constrained. However, financing constraints did not have an impact on productivity for most sectors, with the exception of “R&D and other business activities,” where the negative effect on productivity was large.

13. What can explain these findings? We would expect access to finance to improve productivity by allowing firms to adopt the latest technologies. Therefore, firms facing financing constraints should be less productive since they can not invest in the latest vintages of capital. However, there are a number of reasons why access to finance may not necessarily improve productivity for most sectors. First, in the face of rapid credit growth it is difficult for credit officers to screen clients and ensure that capital is allocated to the most productive activities (see, for example, Ghani and Suri (1999). The rapid buildup in credit thus lowers the quality of investment and reduces the expected productivity gain. Second, higher liquidity may reduce the incentive of shareholders to undertake costly monitoring of managers, which impedes efficient resource allocation and slows productivity growth (Shleifer and Vishny, 1986; and Bhide, 1993). Third, overinvestment and low productivity may also result when managers maximize their own utility rather than firm profits (Grabowsky and Mueller, 1972). Finally, access to finance may increase firms’ production capacity—for example, by expanding plant size—without necessarily increasing productivity (Power, 1998). In fact, macro data indicate that about 50 percent of gross fixed investment during the sample period was on building construction and dwellings, which would not necessarily lead to higher productivity. Overall, these arguments indicate that financially unconstrained firms may not necessarily have higher productivity levels than constrained firms. In the absence of a more explicit estimation model, we cannot distinguish which of these channels is at play in Estonia.

14. Going forward, there is scope for productivity improvements. Estonia’s large TFP gap with respect to the EU-15 underscores its substantial growth potential.14 However, in the face of slowing credit growth, we can expect some firms will confront financing constraints, resulting in lower levels of investment. Nevertheless, productivity levels might not be necessarily lower than in the absence of these constraints. Moreover, these levels could even increase if, in this new environment, banks allocate credit more efficiently—toward more productive firms—or if credit is redirected toward productivity-enhancing projects rather than projects increasing production capacity. However, this may not be enough to offset the other negative factors dampening growth.

DATA SOURCES AND DEFINITIONS

The data used in this paper come from the Estonian Business Registry, which covers the period 1995–2005. Due to missing information on employment for the years 1995–96, we use data only from 1997. We exclude all financial, insurance, and real estate firms, plus public services companies since they are not or less subject to financial constraints, or their investment behavior depends more on political decisions or broader economic policy than on access to (external) finance.15 In addition, we exclude state-owned firms since they are more likely to face soft budget constraints, and are not necessarily profit-maximizing agents. For a detailed description of the construction of the sample, see Moreno Badia and Slootmaekers (2008).

All variables used in this paper are in real terms. Sales, value added, and cash are deflated by output deflator; intermediate inputs are deflated by the intermediate inputs deflator; assets, debt, and investment are deflated with the gross capital formation price index. All deflators come from the system of national accounts provided by the Statistical Office of Estonia, and are available for 16 sectors (corresponding to the one-digit ISIC Rev. 3.1). The following variables are used:

  • Sales (Salesit): net revenue received from the sale of products, goods and services.

  • Labor (Lit): number of employees.

  • Intermediate inputs (Mit): cost of goods, raw materials, and services purchased for core activities.

  • Value added (Yit): net sales minus intermediate inputs.

  • Capital (Kit): tangible and intangible fixed assets minus the goodwill, net of accumulated depreciation.

  • Investment (Iit): calculated based on data on capital and depreciation, Iit = KitKit-1 + Dit, where Dit stands for reported annual depreciation. Due to this calculation, we have no data on investment for the first year of a firm’s observation series.

  • Cash stock (Cashit): sum of the cash stock and short-term financial securities, such as shares at the beginning of period t.

  • Leverage (Leverageit): ratio of long-term liabilities to total assets (net of accumulated depreciation) at the beginning of period t.

  • Age (Ageit): age of the firm at the beginning of period t, based on the entry date in the registry.

  • Size (Sizeit): continuous measure of firm size, measured by total assets (net of accumulated depreciation) at the beginning of period t.

  • Owner (Ownerit): either private, state, foreign, or other. Shareholders with more than 10 percent of share capital of the firm shall be disclosed, and upon this information the Statistical Office of Estonia has classified the ownership type. For example, a firm is labeled foreign if the sum of the foreign-owned shares surpasses 50 percent.

  • Industry classification: Estonian EMTAK code (Classification of Economic Activities of Estonia).

Table A1 provides an overview of the classification of industries used in Estonia.

Table A1.

Industry Classification

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Source: Estonian Business Registry database.

REFERENCES

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1

Prepared by Marialuz Moreno-Badia, based on Moreno Badia and Slootmaekers (2008). We thank Larissa Merkulova and Kadri Rohulaid of the Centre of Registers and Infosystems for the data and valuable clarifications on the Registrar Office’s database.

2

By contrast, according to World Bank data, the percentage financed by internal funds in the other Baltic countries is about 50 percent. Estonia’s dependence on internal funds could be due to financial frictions but may also be explained by the fact that, since 2000, retained earnings are not taxed.

3

According to the same report, access to loans is hindered by many factors, including insufficient guarantees or own capital, short financial histories or insufficient business plans, and financial institutions’ disproportionally large costs of processing small loans.

4

For a description of the data and definitions, see Appendix I.

5

See Stein (2001) for a review of the theoretical literature.

6

See, for example, Fazzari, Hubbard and Peterson (1988); Bond and others (2003); Love (2003); and Forbes (2007).

7

For the derivation of this model and estimation issues, see Moreno Badia and Slootmaekers (2008).

8

The estimated coefficients for the δ’s represents the excess sensitivity of investment in a particular industry to cash flows while the estimated λ’s represents how that excess sensitivity varies with firm characteristics.

9

For example, as can be seen in the last column of Table 3, variation across firms is relatively larger in the “hotels and restaurants” sector.

10

Throughout this section, productivity refers to TFP.

11

A similar approach is used in Fernandes (2007). For details of the estimation procedure and caveats, see Moreno Badia and Slootmaekers (2008).

12

In the interest of brevity, robustness checks are omitted from this paper but can be found in Moreno Badia and Slootmaekers (2008).

13

To a certain extent our results may be driven by firms included in the “other business activities” sector rather than “R&D.” Unfortunately, data availability does not allow us to estimate a separate model for the R&D sector.

14

For a discussion on Estonia’s TFP gap relative to EU-15, see Moreno Badia (2007).

15

More specifically, we exclude the sectors with EMTAK 65 to 70 (financial intermediation and real estate activities) and EMTAK 75 to 99 (public services). See Table A1 for a complete list of the sectors.

Republic of Estonia: Selected Issues
Author: International Monetary Fund
  • View in gallery

    Contribution to Private Sector Credit Growth, 2000:Q4-2008:Q1

    (Year-on-year percent change)

  • View in gallery

    Contributions to Corporate Credit Growth by Industry 1/

    (Year-on-year, percent change)

  • View in gallery

    Labor Productivity

    (Sales per worker, thousands of Estonian krooni)

  • View in gallery

    Mean Financing Constraints by Industry, 1998-2005 1/

    (Sales per worker, thousands of Estonian krooni)