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Demand uncertainty and the capital–labour ratio in Poland
Emerging Markets Review 2 Ž2001. 183᎐196
Demand uncertainty and the capital᎐labour ratio in Poland Christopher J. Green a , Robert Lensink b, Victor Murinde c,U a
Department of Economics, Loughborough Uni¨ ersity, Loughborough, UK Faculty of Economics, Uni¨ ersity of Groningen, Groningen, The Netherlands c Birmingham Business School, Uni¨ ersity of Birmingham, Edgbaston, Birmingham B15 2TT, UK b
Received 8 November 2000; received in revised form 15 March 2001; accepted 15 March 2001
Abstract This paper investigates the effect of demand uncertainty on the capital᎐labour ratio of non-financial firms in Poland. An eclectic model is used to characterise a utility maximising firm in a transition economy with demand uncertainty and imperfect competition. It is assumed that labour is completely variable and capital is quasi-fixed. The demand for capital, and hence the capital᎐labour ratio, derives from the optimisation of expected costs and the firm’s pricing and output decisions, and crucially depends on the sign of the covariance term, i.e. the firm’s risk behaviour. The main testable proposition of the model is that if firms are risk-lovers, an increase in demand uncertainty increases the capital᎐labour ratio, whereas the capital᎐labour ratio decreases when firms are risk-averse. The model is estimated using data from a cross-section of 148 non-financial firms in Poland. The results unambiguously show that there exists a significant positive relationship between demand uncertainty and the capital᎐labour ratio. This finding suggests that Polish firms are risklovers, i.e. they respond to demand uncertainty by increasing their capital᎐labour ratio because they are more concerned to have stable labour costs than they are to have stable profits. The evidence has important implications for the needed set of regulations and corporate governance in Poland as part of the necessary economic reform. 䊚 2001 Elsevier Science B.V. All rights reserved. JEL classification: D81 Keywords: Demand uncertainty; Capital᎐labour ratio; Poland; Risk-averse; Risk-lovers U
Corresponding author. Tel.: q44-121-414-6704; fax: q44-121-414-6238. E-mail address: [email protected] ŽV. Murinde.. 1566-0141r01r$ - see front matter 䊚 2001 Elsevier Science B.V. All rights reserved. PII: S 1 5 6 6 - 0 1 4 1 Ž 0 1 . 0 0 0 1 6 - 4
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1. Introduction A key policy issue in the emerging market economies of eastern and central Europe is to engender the transition to the price mechanism for the allocation of resources. For policy makers, an important part of the transition process involves the design of the needed set of regulations and corporate governance in the face of extreme forms of uncertainty, especially characterised by unstable market conditions in which firms operate. For example, firms are concerned about conditions of demand uncertainty as well as input choices. In this context, government regulations seem to be necessary to facilitate a stable business environment, promote business confidence and optimism and thus generate a stable growth path for the transition economies. The main literature that may be invoked to shed light on the above economic problems, at the firm level, relates to models of firm behaviour under uncertainty, and shows that demand uncertainty affects firms’ input choices and cost functions Žsee, e.g. Holthausen, 1976; Ghosal, 1991, 1995.. A common element of these models is that they put forward some plausible conditions under which stochastic demand conditions may determine the capital᎐labour ratios. For example, when capital is chosen ex ante but labour is determined after the level of demand is observed, firms may respond to demand uncertainty by using a smaller amount of capital and thus operate with a lower capital᎐labour ratio in a sub-optimal manner. However, the special line of inquiry taken by the early literature, for example Holthausen Ž1976., argues that the way in which firms react to stochastic demand conditions depends on their risk behaviour. This suggests that more information about firm’s risk behaviour may be an important input in the development of an efficient system of government regulation. In the context of emerging market economies, effective regulation may minimise the risk exposure of firms. In contrast, recent literature, e.g. Ghosal Ž1991, 1995., introduces the argument of firm size into the analysis, arguing that an increase in firm size counteracts the negative relationship between demand uncertainty and the capital᎐labour ratio. This paper investigates the effect of demand uncertainty on the capital᎐labour ratio of firms in Poland. The motivation of the paper is that, under given conditions characterising the economic transition process in Poland, the impact of demand uncertainty on the capital᎐labour ratio sheds light on the firms’ risk behaviour. It is argued that if firms are risk-lovers, an increase in demand uncertainty increases the capital᎐labour ratio, whereas the capital᎐labour ratio would decrease when a firm is risk-averse. Hence, the main proposition put forward in the paper is that the elasticity of the capital᎐labour ratio with respect to demand uncertainty serves as an indirect indicator of the risk behaviour of Polish firms. The empirical results of the paper show that there is a significant positive relationship between demand uncertainty and the capital᎐labour ratio of Polish firms. This finding suggests that Polish firms are risk-lovers. We conclude that the evidence may have important consequences for the needed set of regulations and corporate governance in Poland. The paper makes at least three major contributions. First, it introduces an
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important variation to recent research on firm behaviour under demand uncertainty Že.g. Ghosal, 1991, 1995. by focusing on the risk behaviour Žspecifically, risk-averse vs. risk-loving. of firms under conditions which are characteristic of transition economies such as Poland. Second, while the literature on input choices and the stochastic nature of demand has generated a number of theoretical models, the empirical dimension is less well developed; in this context, this paper contributes to recent efforts to provide econometric evidence that may facilitate the evaluation of these models. Third, the paper presents evidence which, in some respects, calls for a re-examination of the findings of previous work on demand uncertainty and factor input choices. The remainder of this paper is structured as follows. Section 2 discusses the underlying theoretical model Žpresented in Appendix A. and sets out the empirical equation. The dataset and empirical procedures, including the method for estimating demand uncertainty, are described in Section 3. Section 4 presents the estimation results. Section 5 concludes.
2. The empirical model Appendix A presents the theoretical model that forms the basis of the empirical equation. We only describe here the most important features of the model. The purpose of the model is to show that risk-loving firms faced with demand uncertainty prefer to have a high capital᎐labour ratio, whereas risk-averse firms prefer low capital᎐labour ratios. The model characterises a representative utility maximising firm in a transition economy with uncertain demand conditions; the firm is a price setter under imperfect competition. It is assumed that labour is completely variable and capital is quasi-fixed, and the firm chooses capital input before actual demand is observed. Labour demand is not considered as a decision variable in the sense that after actual demand is observed, the capital stock is set and labour input is chosen to satisfy the level of demand. The demand for capital, and hence the capital᎐labour ratio, derives from the optimisation of expected costs and the firm’s pricing and output decisions, and crucially depends on the sign of the covariance term, i.e. the risk behaviour of the firm. There are three possibilities: Ž1. If the optimisation process yields a negative covariance term, the result corresponds to a concave function and risk-averse behaviour, indicating that less capital is used than the cost minimising outcome. Ž2. If the optimisation process yields a positive covariance term, the result corresponds to a convex function and risk-loving behaviour of the firm, indicating that the capital᎐labour ratio is above the cost minimising level. Ž3. If the optimisation process yields a zero covariance term, the capital᎐labour ratio equals the cost minimising level, implying that utility is linear and the firm is risk-neutral. It is useful here to clarify the point that the objective function of the firm is to maximise utility, as opposed to the more standard assumption of maximising profits. We choose the former rather than the latter because, under conditions of
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uncertainty, expected utility maximisation is the most convenient simple way to characterise behaviour. It has the advantage of encompassing interesting special cases such as risk-aversion, risk-neutrality, and risk-loving behaviour. Expected profit maximisation is equivalent to risk-neutral behaviour Žsee, e.g. Hirshleifer and Riley, 1992.. Therefore, if the firm’s objective were to maximise expected profits, it would not admit the various special cases which we wish to test.1 Clearly, using expected profit maximisation instead of expected utility maximisation would make a difference to the results of the model, in that it would rule out the interesting cases which we test. This can be seen in Eqs. ŽA8. and ŽA9. in Appendix A. Under profit maximisation U⬙ is, by construction, identically zero, hence so is covŽ l,U⬘..2 The intuition behind the result that risk aversion Žloving. implies a lower Žhigher. capital᎐labour ratio is as follows. Under demand uncertainty, fluctuations in demand lead to fluctuations in output, revenues, costs, and profit. Risk-averse firms choose a capital stock to reduce profit fluctuations. In our model, the volatility of profits depends on how volatile costs are relative to revenues. The volatility of revenues Ždetermined by pu. is exogenous in the model. The capital stock is fixed, and labour is adjusted to keep the firm on the production function, given output, which is set equal to the exogenously fluctuating level of demand. Therefore, the volatility of costs depends on the volatility of labour input. The higher is the capital᎐labour ratio, the less volatile is labour input, for a given capital stock, in relation to output fluctuations. In other words, the smaller is ⭸Ž⭸qr⭸l .r⭸Ž krl .. This can be seen from Eq. ŽA5. in Appendix A, or more directly by differentiating the production function, or by drawing the usual isoquants in capital᎐labour space. Thus, the higher is the capital᎐labour ratio, the more volatile are profits, because costs become increasingly less volatile than revenues. Paradoxically therefore, risk-averse firms in our model will wish to have more volatile costs Žthrough a lower capital᎐labour ratio., as this will mean that costs move more in line with revenues, thus leading to less volatility in profits. Clearly, if firms are risk-averse and the capital᎐labour ratio is less than the cost-minimising outcome, then, as output, labour input, costs, and profits increase, the marginal utility of profits decreases as the risk-averse firm moves up the profit function, giving a negative covariance between marginal utility and labour input. If firms are risk-lovers, then the marginal utility of profit will increase as firms move up the profit function as output rises, giving a positive covariance between marginal utility and labour input. This is the link we exploit to generate testable hypotheses. The testable proposition of the model, therefore, is that risk-loving firms faced with demand uncertainty prefer to have high capital᎐labour ratios, whereas risk-
1
It may be argued that even with profit maximizing it is possible to account for risk-aversion by not using a constant discount rate in a dynamic optimisation framework. However, this is not the case in this paper as our model has static optimisation. 2 This point is emphasised in Appendix A in the paragraph following Eq. ŽA10..
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averse firms prefer low capital᎐labour ratios. To represent this proposition, a corresponding empirical equation can be specified as follows: Ž KrL . i s  0 "  1UNC i q  2Wi q i
Ž1.
where UNC denotes a measure of demand uncertainty; W is the cost per employee and serves as a control variable in estimation and testing; is an error term; and i s 1, 2, 3,....., n firms. The sign of  1 is theoretically dichotomous Ž". and captures the competing hypotheses on the risk behaviour of firms: 3 if the firms are risk-lovers, the sign is positive; a negative sign indicates risk-averse firms.
3. The dataset and empirical procedures We use the dataset AMADEUS which contains firm level financial data for more than 200 000 non-financial firms in Europe, including Poland. The data are provided by local bureaus and AMADEUS compiles the dataset by only including companies which satisfy one of the following size criteria. 1. a turnover greater than 10 million Euro; 2. a number of employees greater than 150; 3. a total asset sum greater than 10 million Euro. Uniformity is achieved by standardisation of accounting information. A big advantage of the dataset is that it comprises listed as well as unlisted firms. For Poland, where there is only a small amount of firms listed on the stock market, this implies that many more firms can be taken into account in the analysis. However, the fact that the dataset focuses on unlisted firms is for some types of analysis also a drawback. For example, the dataset does not contain market values; this limitation precludes analyses for which Tobin’s Q is required. Another drawback of this dataset is that, for most countries including Poland, it only contains yearly data for 1992᎐1996. Obviously, this is a main shortcoming. But it should be taken into account that for most eastern European countries, given the structural break associated with the collapse of the communist system, it would be questionable whether data for the pre-1990 period, if they were available, could be useful at all. Due to the limited time period for which the data are available, our analysis is based on mean values for the 1992᎐1996 period. We calculate the mean value for all original variables prior to any other transformation. By using means, cyclical 3 In some recent studies, the theoretical dichotomy is not addressed. For example, Ghosal Ž1991. postulates only a negative relationship between demand uncertainty and the capital᎐labour ratio. However, unlike this paper, the model by Ghosal Ž1991. is specified as Ž KrL. s f ŽUNC, SIZE. where SIZE is a measure of firm size, and serves to counteract the negative relationship between demand uncertainty and the capital᎐labour ratio.
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influences are avoided.4 All data are denominated in millions of current US dollars. Since US inflation has been moderate in the sample period, this transformation helps to avoid inflation problems. The calculation of one important variable in the analysis ᎏ demand uncertainty ᎏ requires time series data. In order to be able to make a reasonable estimate of demand uncertainty, we only consider firms for which at least 4 years of the relevant data are available. Moreover, we balance the data set so that in all regressions the same amount of firms are taken into account. Finally, to account for extreme outliers, we delete from all variables used in the estimates the five lowest and the five highest values. The final dataset contains 148 Polish firms. In order to determine the most appropriate method for measuring uncertainty, we consider the following methods used in the literature Žsee Lensink et al., 2001.: 1. 2. 3. 4. 5.
the S.D. of the unpredictable part of a stochastic process; the S.D. of the variable under consideration; the S.D. from a geometric Brownian process; the S.D. derived from survey data; the Generalised Auto-Regressive Conditional Heteroskedastic ŽGARCH. model of volatility.
In this paper uncertainty is measured using the first rather than the second method in order to represent a stochastic process. We do not use the third method because the derivation of uncertainty from a geometric Brownian process requires continuous data, which we do not have for this study. We also rule out the fourth method because this paper does not use survey data. We could have used a GARCH model to forecast uncertainty. However, the GARCH model is especially relevant for high frequency data which display clustering effects, whereas the data set used in this paper contains annual observations.5 Specifically, the approach we use to construct the demand uncertainty variable consists of two steps. In the first step, we formulate and estimate a forecasting equation which determines the expected part of the variable under consideration. In line with many other empirical studies Žsee, among others, Aizenman and Marion, 1993; Ghosal, 1995; Ghosal and Loungani, 1996., we use an autoregressive process as the forecasting equation. Due to the limited time series data available
4
By avoiding cyclical fluctuations, we are able to focus on the underlying nature of the relationship between demand uncertainty and the capital᎐labour ratio. Moreover, the period of 1992᎐1996, for which the data are available, is not characterised by any important cyclical downturns in Poland or the rest of the world economy. 5 The data period is too short to allow directly for time variation in uncertainty. Moreover, since we estimate the model as a single cross-section it is not possible to allow for time-varying uncertainty. The measure of uncertainty we use is the S.D. of the difference between the actuals and the conditional forecast, and therefore does allow for information in the dataset, but only to be updated outside the timespan of the dataset and not within it.
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per firm, we use a first-order autoregressive process. We consider two alternative specifications, with and without a constant. lnSAL t s a1 lnSAL ty1 q e t
Ž2.
lnSAL t s a2 q a3 lnSAL ty1 q t
Ž3.
where SAL denotes sales of firms; a1 and a3 are the autoregressive parameters; a2 is a constant; ln denotes natural logarithm; and e t and t are error terms.6 We estimate the above given equations for all firms in our dataset. In the second step of our procedure, the proxies for demand uncertainty are obtained by calculating the S.D. of the unexpected part of SAL, i.e. the residuals from the forecasting Eqs. Ž2. and Ž3., respectively. Hence, UNC1 refers to the proxy obtained from the equation with a constant, while UNC refers to the proxy obtained from the equation without a constant.7 In all, therefore, we initially extract the following set of variables from the dataset: K, tangible assets; L, the number of employees; CEMPL, the total cost of employees; and SAL, sales. Basing on these variables, we construct the variables used in the estimates, namely CLRAT, the capital᎐labour ratio, calculated as KrL; W, cost per employee, calculated as CEMPLrL and used as a control variable in the estimates; and UNC and UNC1 which denote the proxies for demand uncertainty, calculated by using SAL, as explained above. In Table 1 we give some statistical moments of the main variables used in estimating the model. The first and second moments Žmean and S.D.. suggest fairly good dispersion characteristics for a cross-section sample of 148 Polish firms. For example, the capital᎐labour ratio ranges from a minimum of 0.0012 to a maximum of 0.064 with a mean value of 0.013 and a S.D. of 0.0107; demand uncertainty measured without a constant ranges from 0.85 to a maximum of 78.62 with a mean value of 9.64 and a S.D. of 12.19; the measure of demand uncertainty that includes a constant range from 0.31 to 50.51 with a mean value of 5.96 and S.D. of 8.23; wages range from 0.0025 to 0.0107 with a mean of 0.0044 and a S.D. of 0.0014. The results thus show a wide diversity in the indicators, suggesting extreme outliers in the data are unlikely.
4. Estimation results Although the economic theory discussed in Section 2 yields hypotheses on the effect of demand uncertainty and wages on the capital᎐labour ratio, the economic 6 Given the short span of the data for each firm Žfive annual observations., we did not experiment with more complicated specifications of the autoregressive process, e.g. fitting a linear trend or specifying a quadratic function to capture the fluctuations of sales around the trend. 7 As observed by Aizenman and Marion Ž1993. the main shortcoming of this method is that future changes in the environment that are fully anticipated are still treated as uncertain.
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Table 1 Statistical moments of the main variables used in model estimation a
UNC UNC1 CLRAT W
Mean
Median
S.D.
Max
Min
9.64 5.96 0.013 0.0044
5.96 3.55 0.010 0.0039
12.19 8.23 0.0107 0.0014
78.62 50.51 0.064 0.0107
0.85 0.31 0.0012 0.0025
a UNC denotes the proxy for demand uncertainty obtained by estimating the sales ŽSAL. equation without a constant; UNC1 refers to the proxy for demand uncertainty obtained by estimating the SAL equation with a constant; CLRAT is the capital᎐labour ratio, calculated as KrL, where K is tangible assets and L is the number of employees; W is cost per employee, calculated as CEMPLrL, where CEMPL is the total cost of employees.
functional relationship is not completely specified. We therefore empirically explore the exact form of the estimating equation by experimenting with variants of Eq. Ž1. in linear, quasi-logarithmic and logarithmic forms. In all cases, however, we do not find it plausible to include in the model an interaction between demand uncertainty and wages as an additional argument in Eq. Ž1., i.e.  3 ŽUNC = W . i . Table 2 presents the estimation and testing results. The results show that the fit of the equations is generally good in terms of the F-statistic; the adjusted R 2 is low, which is typical of cross-section regressions of this type. Given that heteroskedasticity could be important across firms, the t-values were computed from White’s heteroskedasticity-consistent standard errors ŽWhite, 1980.; the t-statistics show that all the coefficients are significant. The evidence is unambiguous. Irrespective of whether the empirical model is specified in its linear, quasi-logarithmic, or logarithmic variants, the evidence shows that there exists a significant positive relationship between demand uncertainty and the capital᎐labour ratio. In this specific study, therefore, the evidence resolves the theoretical dichotomy in Eq. Ž1. by showing that with respect to a sample of 148 firms in the transition economy of Poland, the sign of  1 is empirically positive, with statistically significant coefficients. The evidence is therefore unequivocal that Polish firms are risk-lovers; with an increase in demand uncertainty, the firms tend to increase their capital᎐labour ratio. The logarithmic variants of the model offer additional information about the elasticities. For model variant Ž5. in Table 2, when demand uncertainty is measured on the basis of Eq. Ž2. without a constant in the first-order autoregressive process, it is shown that, on average, a 1% rise in demand uncertainty will result in a 0.22% increase in the capital᎐labour ratio. The elasticity falls to 0.15 when demand uncertainty is measured on the basis of Eq. Ž2., without a constant in the first-order autoregressive process, as in model variant Ž6..8 The F-test statistic indicates that model variant Ž5. outperforms the rest of the model variants considered in this 8
In general, however, the results reported in Table 2 are reliably robust to the specific measure of demand uncertainty, i.e. UNC or UNC1.
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Table 2 Estimation results for a cross-section sample of 148 Polish firms a Ž1. CLRAT UNC
Ž2. CLRAT
0.00020 Ž2.15.
UNC1
Ž3. LnŽCLRAT.
Ž4. LnŽCLRAT.
Ž5. LnŽCLRAT.
0.0247 Ž3.12. 0.00026 Ž2.01.
0.0188 Ž3.06.
lnŽUNC.
0.2288 Ž3.50.
lnŽUNC1.
W
0.1578 Ž2.81. 1.137 Ž1.91.
1.198 Ž1.95.
94.11 Ž2.34.
98.49 Ž2.37.
lnŽW .
Constant
F Adj. R2
Ž6. LnŽCLRAT.
0.0064 Ž2.40.
0.0066 Ž2.35.
y5.14 Žy27.34.
y5.13 Žy26.30.
0.531 Ž2.72.
0.614 Ž3.00.
y2.09 Žy1.92.
y1.04 Žy1.27.
6.37
5.24
7.59
6.40
11.26
8.39
0.07
0.06
0.08
0.07
0.12
0.09
a
t-values are between parentheses, below the estimated coefficients; the values are based on White’s heteroskedasticity consistent standard errors. Eqs. Ž1᎐6. in the table are variants of the following generic model: CLRATi,t s  0 " 1 UNC i,t q  2 Wi,t q i,t , where CLARET is the capital᎐labour ratio; UNC is a measure of demand uncertainty; W is a labour cost variable; subscripts i,t denote firm i at time t; and is the error term.
study. As theoretically postulated in Sections 2 and 3, the labour costs variable is empirically positive for all model variants explored in this paper. In general, our empirical results can be interpreted to mean that firms in Poland are risk-lovers with respect to profits rather than labour costs. In other words, the firms respond to demand uncertainty by increasing their capital᎐labour ratio because they are more concerned to have stable labour costs than they are to have stable profits. This seems to us to be a reasonable finding in the context of Poland, and eastern Europe transition economies in general, given that the labour force, which were previously employed in government-owned corporations, have the wrong skills and are costly to retrain to be able to work productively for private sector companies in a market economy; there are also high ancillary costs of hiring and firing, among other labour costs.
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An alternative explanation of our results takes us back to the theory underpinning our empirical equation, and relates to the flexibility of labour relative to capital. If labour can adjust to price shocks, fluctuations lead the firm to change the labor᎐capital ratio, so the marginal product of capital changes more than price changes. Hence, in making their input choices following an increase in demand uncertainty, Polish firms tend to increase their capital᎐labour ratio.
5. Summary and conclusion This paper investigates the effect of demand uncertainty on the capital᎐labour ratio of non-financial firms in Poland. A generic model is used to characterise a representative firm operating under imperfect competition and uncertain demand conditions in a transition economy. The main testable proposition of the model is that if firms are risk-lovers, an increase in demand uncertainty increases the capital᎐labour ratio, whereas the capital᎐labour ratio decreases if firms are risk-averse. Using cross-section data for 148 non-financial Polish firms, we test variants of the model in order to estimate the elasticity of the capital᎐labour ratio with respect to demand uncertainty. The econometric results resolve the theoretical dichotomy and show that an increase in demand uncertainty leads to an increase in the capital᎐labour ratio of Polish firms. This finding suggests that Polish firms are risk-lovers. In general, the results are robust to the functional form of model specification Žlinear, semi-logarithmic and logarithmic. as well as the measure of demand uncertainty. The knowledge of how firms react to uncertain demand conditions, uncovered in this study, offers a number of policy implications for Poland and some other transition economies. First, the evidence may have important consequences for the needed set of regulations and corporate governance in Poland as part of the necessary economic management during the transition period. Regulatory policy may be applied to influence the level of demand uncertainty as well as factor demand and factor productivity. For example, an introduction of a sales tax is expected to operate directly on the sales volume of firms and hence on the fluctuation of actual sales, and indirectly on the unexpected component of the sales Žor demand uncertainty.. Since demand uncertainty directly affects the capital᎐labour ratio, the effect may work its way to the demand for, and the pricing of, the firm’s factor inputs such as capital and labour Že.g. employment by firms. and the productivity of these factors. Second, the results shed light on how risk-loving firms behave in the face of a downturn in business confidence. The measure of demand uncertainty used in this paper derives from the unpredictable component of sales by firms and can therefore serve as a useful indicator of business confidence and consumer optimism. An increase in demand uncertainty reduces business confidence but risk-loving firms tend to increase their capital᎐labour ratio. Third, the results imply that, under demand uncertainty, firm managers need to be cautious in making their input policies as these choices
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influence firm output, costs, revenues and profitability. For example, under demand uncertainty, risk-averse firms choose a capital stock to reduce fluctuations in output, revenues, costs, and profit. But because the capital stock is fixed in the short run in the sense that transition economies are characterised by the existence of a part of capital stock which is unproductive, labour is adjusted to keep the firm on the production function. Therefore, managers need to address the volatility of labour inputs in order to come to grips with the volatility of costs; precisely, in making policies about the firms’ factor proportions, managers should be concerned more about fluctuations in labour costs than fluctuations in profits. A fourth important policy implication of our empirical result is that the risk-taking behaviour of firms in Poland with respect to higher capital᎐labour ratios following an increase in demand uncertainty may well be stimulated by the weak legal framework for enforcing financial discipline by firms, and the inability of government to implement insolvency and bankruptcy codes. However, to be able to refine the regulatory and other implications of this study, further and more detailed econometric research is necessary.
Acknowledgements We thank the editor ŽJavier Estrada. and an anonymous referee of this journal for useful comments on a previous version of the paper; constructive criticisms were also received from seminar participants at the University of Birmingham, Tallinn Technical University, University of Economics at Poznan and University of Groningen. We gratefully acknowledge financial support from the European Commission’s PharerACE Programme 1997᎐1999 under Contract Number 966152-R. At the time of writing this paper, Robert Lensink was on sabbatical at Loughborough University. However, the interpretations and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the European Commission.
Appendix A. An eclectic theoretical model
We use an eclectic model based on Holthausen Ž1976.. However, our model differs from the one by Holthausen Ž1976. in the sense that, for reasons of convenience, we specify the main equations which capture the business environment in emerging market economies. The model assumes that the firm is an imperfect competitor who behaves as a price setter. The firm operates in a world where demand is uncertain. Its demand function is therefore specified as: q s u y p
Ž A1.
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where q is total demand, u is a random term and p is the price level. The firm produces goods with a standard neoclassical Cobb᎐Douglas production function with two inputs, capital Ž k . and labour Ž l .: q s k ␣ l Ž 1y␣ .
Ž A2.
A crucial assumption is that labour is completely variable, whereas capital is quasi-fixed. This is taken into account by assuming that capital input is chosen before actual demand is observed, whereas the demand for labour takes place after demand is observed. If the capital stock is set, and actual demand is observed, labour input will always be chosen so that any level of demand is satisfied. To simplify the analysis, labour demand is not considered as a decision variable, but is taken into account implicitly by a labour-requirements function, which is specified as: l s Ž qrk ␣ .Ž 1r1y␣ .
Ž A3.
The firm optimises the expected utility of profit Ž ⌸ .: Max Ž p ,k . EU Ž ⌸ .
Ž A4.
where U is a von Neumann᎐Morgenstein utility function, and E is an expectations operator. The decision variables are the price level and the capital stock. Since the firm is assumed to optimise the utility of profits, we are able to consider the implications of different assumptions regarding the risk behaviour of a firm and the consequences for the capital᎐output ratio. We use Von Neumann Morgenstern utility function. Such a utility function satisfies several conditions. The first is that there is non-satiation, which means that more is preferred over less. Mathematically, this implies a positive first derivative. A strictly concave Von Neumann Morgenstern utility function Žnegative second derivative. implies risk aversion. Risk-loving behaviour implies a convex utility function Žpositive second derivative. and risk-neutral behaviour corresponds to a linear utility function Žsecond derivative is zero.. If we had assumed that firms maximise profits, the analysis would be restricted to a risk-neutral firm. Profit is defined as: ⌸ s p Ž u y p . y w wŽ u y p . rk ␣ xŽ 1r1y␣ . y ic Ž k .
Ž A5.
where w is the wage rate; c is the price of capital; and i is the cost of capital. The demand function and the labour requirement functions are taken into account. It is assumed that w, c, i are given for the firm. The first-order condition with respect to k is:
½
E U⬘ Ž ⌸ . w
ž
␣
l
1y␣ k
y ic
/5
s0
Ž A6.
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For two random variables, EŽ X,Y . s EŽ X . EŽ Y . q covŽ X,Y ., so that the above equation can be rewritten as: EU⬘ Ž ⌸ . E w
ž
␣
l
1y␣ k
y ic q w
/
␣ 1y␣
cov
ž
l k
,U⬘ Ž ⌸ . s 0
/
Ž A7.
so that: 1 E
l k
s
1 y ␣ ic ␣
w
y
k
cov Ž l,U⬘ Ž ⌸ .. Ž A8.
EU⬘ Ž ⌸ .
It follows that, on the one hand, the demand for capital is greater than the amount of capital the firm uses if expected costs are minimised for a given level of output in the case where the left-hand side of the above expression is smaller than icrw. Since, U⬘Ž ⌸ . ) 0, a firm will demand more capital than the cost minimising amount when the covariance term is positive. On the other hand, in the case where the left-hand side of the expression in Eq. ŽA8. is greater than icrw, the demand for capital is smaller than the amount of capital the firm uses if expected costs are minimised for a given level of output. Since, U⬘Ž ⌸ . ) 0, a firm will demand less capital than the cost minimising amount when the covariance term is negative. The crucial indicator, therefore, is the sign of the covariance term. The covariance term can be signed by considering the effects of an increase in the random term. Hence: ⭸U⬘ Ž ⌸ . ⭸u
s
⭸⌸ ⭸u
U⬙ Ž ⌸ . s p y w
⭸L Ž q,k .
⭸q Ž p,u .
⭸q
⭸u
U⬙ Ž ⌸ .
Ž A9.
The first term in brackets on the right-hand side of the above equation is always positive under the assumption that the price is higher than the marginal cost of production. Since qu ) 0, the sign of ␦U⬘Ž ⌸ .r␦u depends on the sign of U⬙ Ž ⌸ .. It follows that: ⭸l ⭸u
s
⭸l ⭸q ⭸q ⭸u
s
1
q
ž /
1 y ␣ k␣
␣r Ž1y␣ .
ky␣ G 0
Ž A10.
Hence, the covariance depends on U⬙ Ž ⌸ .. If U⬙ Ž ⌸ . - 0, which corresponds to a concave function and risk-averse behaviour, the covariance term is negative. In that case less capital is used than the cost-minimising outcome. If U⬙ Ž ⌸ . ) 0, and hence the firm is risk-loving, the covariance term is positive, and accordingly the capital labour ratio is above the cost-minimising level. If U⬙ Ž ⌸ . s 0, U⬘Ž ⌸ . s constant, implying that utility is linear, the firm is risk-neutral; in that case the
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covariance term equals zero, so that the capital᎐labour ratio equals the cost-minimizing level.9
References Aizenman, J., Marion, N.P., 1993. Macroeconomic uncertainty and private investment. Econ. Lett. 41, 207᎐210. Ghosal, V., 1991. Demand uncertainty and the capital᎐labor ratio: evidence from the US manufacturing sector. Rev. Econ. Stat. 73, 157᎐160. Ghosal, V., 1995. Input choices under price uncertainty. Econ. Inq. 33, 142᎐158. Ghosal, V., Loungani, P., 1996. Product market competition and the impact of price uncertainty on investment: some evidence from US manufacturing industries. J. Ind. Econ. 44, 217᎐228. Hirshleifer, J., Riley, J.G., 1992. The Analytics of Uncertainty and Information. Cambridge University Press, Cambridge. Holthausen, D.M., 1976. Input choices and uncertain demand. Am. Econ. Rev. 66, 94᎐103. Lensink, R., Bo, H., Sterken, E., 2001. Investment, Capital Market Imperfections and Uncertainty. Edward Elgar Žforthcoming., Cheltenham. White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817᎐838. 9 Note that strictly speaking the above theoretical analysis gives the o¨ erall impact, rather than the marginal impact, of uncertainty on the capital᎐labour ratio. However, the empirical Eq. Ž1. and the regressions in the empirical part of the paper reflect the marginal Žpartial. effects of demand uncertainty on the capital᎐labour ratio.
Demand uncertainty and the capital᎐labour ratio in Poland Christopher J. Green a , Robert Lensink b, Victor Murinde c,U a
Department of Economics, Loughborough Uni¨ ersity, Loughborough, UK Faculty of Economics, Uni¨ ersity of Groningen, Groningen, The Netherlands c Birmingham Business School, Uni¨ ersity of Birmingham, Edgbaston, Birmingham B15 2TT, UK b
Received 8 November 2000; received in revised form 15 March 2001; accepted 15 March 2001
Abstract This paper investigates the effect of demand uncertainty on the capital᎐labour ratio of non-financial firms in Poland. An eclectic model is used to characterise a utility maximising firm in a transition economy with demand uncertainty and imperfect competition. It is assumed that labour is completely variable and capital is quasi-fixed. The demand for capital, and hence the capital᎐labour ratio, derives from the optimisation of expected costs and the firm’s pricing and output decisions, and crucially depends on the sign of the covariance term, i.e. the firm’s risk behaviour. The main testable proposition of the model is that if firms are risk-lovers, an increase in demand uncertainty increases the capital᎐labour ratio, whereas the capital᎐labour ratio decreases when firms are risk-averse. The model is estimated using data from a cross-section of 148 non-financial firms in Poland. The results unambiguously show that there exists a significant positive relationship between demand uncertainty and the capital᎐labour ratio. This finding suggests that Polish firms are risklovers, i.e. they respond to demand uncertainty by increasing their capital᎐labour ratio because they are more concerned to have stable labour costs than they are to have stable profits. The evidence has important implications for the needed set of regulations and corporate governance in Poland as part of the necessary economic reform. 䊚 2001 Elsevier Science B.V. All rights reserved. JEL classification: D81 Keywords: Demand uncertainty; Capital᎐labour ratio; Poland; Risk-averse; Risk-lovers U
Corresponding author. Tel.: q44-121-414-6704; fax: q44-121-414-6238. E-mail address: [email protected] ŽV. Murinde.. 1566-0141r01r$ - see front matter 䊚 2001 Elsevier Science B.V. All rights reserved. PII: S 1 5 6 6 - 0 1 4 1 Ž 0 1 . 0 0 0 1 6 - 4
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1. Introduction A key policy issue in the emerging market economies of eastern and central Europe is to engender the transition to the price mechanism for the allocation of resources. For policy makers, an important part of the transition process involves the design of the needed set of regulations and corporate governance in the face of extreme forms of uncertainty, especially characterised by unstable market conditions in which firms operate. For example, firms are concerned about conditions of demand uncertainty as well as input choices. In this context, government regulations seem to be necessary to facilitate a stable business environment, promote business confidence and optimism and thus generate a stable growth path for the transition economies. The main literature that may be invoked to shed light on the above economic problems, at the firm level, relates to models of firm behaviour under uncertainty, and shows that demand uncertainty affects firms’ input choices and cost functions Žsee, e.g. Holthausen, 1976; Ghosal, 1991, 1995.. A common element of these models is that they put forward some plausible conditions under which stochastic demand conditions may determine the capital᎐labour ratios. For example, when capital is chosen ex ante but labour is determined after the level of demand is observed, firms may respond to demand uncertainty by using a smaller amount of capital and thus operate with a lower capital᎐labour ratio in a sub-optimal manner. However, the special line of inquiry taken by the early literature, for example Holthausen Ž1976., argues that the way in which firms react to stochastic demand conditions depends on their risk behaviour. This suggests that more information about firm’s risk behaviour may be an important input in the development of an efficient system of government regulation. In the context of emerging market economies, effective regulation may minimise the risk exposure of firms. In contrast, recent literature, e.g. Ghosal Ž1991, 1995., introduces the argument of firm size into the analysis, arguing that an increase in firm size counteracts the negative relationship between demand uncertainty and the capital᎐labour ratio. This paper investigates the effect of demand uncertainty on the capital᎐labour ratio of firms in Poland. The motivation of the paper is that, under given conditions characterising the economic transition process in Poland, the impact of demand uncertainty on the capital᎐labour ratio sheds light on the firms’ risk behaviour. It is argued that if firms are risk-lovers, an increase in demand uncertainty increases the capital᎐labour ratio, whereas the capital᎐labour ratio would decrease when a firm is risk-averse. Hence, the main proposition put forward in the paper is that the elasticity of the capital᎐labour ratio with respect to demand uncertainty serves as an indirect indicator of the risk behaviour of Polish firms. The empirical results of the paper show that there is a significant positive relationship between demand uncertainty and the capital᎐labour ratio of Polish firms. This finding suggests that Polish firms are risk-lovers. We conclude that the evidence may have important consequences for the needed set of regulations and corporate governance in Poland. The paper makes at least three major contributions. First, it introduces an
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important variation to recent research on firm behaviour under demand uncertainty Že.g. Ghosal, 1991, 1995. by focusing on the risk behaviour Žspecifically, risk-averse vs. risk-loving. of firms under conditions which are characteristic of transition economies such as Poland. Second, while the literature on input choices and the stochastic nature of demand has generated a number of theoretical models, the empirical dimension is less well developed; in this context, this paper contributes to recent efforts to provide econometric evidence that may facilitate the evaluation of these models. Third, the paper presents evidence which, in some respects, calls for a re-examination of the findings of previous work on demand uncertainty and factor input choices. The remainder of this paper is structured as follows. Section 2 discusses the underlying theoretical model Žpresented in Appendix A. and sets out the empirical equation. The dataset and empirical procedures, including the method for estimating demand uncertainty, are described in Section 3. Section 4 presents the estimation results. Section 5 concludes.
2. The empirical model Appendix A presents the theoretical model that forms the basis of the empirical equation. We only describe here the most important features of the model. The purpose of the model is to show that risk-loving firms faced with demand uncertainty prefer to have a high capital᎐labour ratio, whereas risk-averse firms prefer low capital᎐labour ratios. The model characterises a representative utility maximising firm in a transition economy with uncertain demand conditions; the firm is a price setter under imperfect competition. It is assumed that labour is completely variable and capital is quasi-fixed, and the firm chooses capital input before actual demand is observed. Labour demand is not considered as a decision variable in the sense that after actual demand is observed, the capital stock is set and labour input is chosen to satisfy the level of demand. The demand for capital, and hence the capital᎐labour ratio, derives from the optimisation of expected costs and the firm’s pricing and output decisions, and crucially depends on the sign of the covariance term, i.e. the risk behaviour of the firm. There are three possibilities: Ž1. If the optimisation process yields a negative covariance term, the result corresponds to a concave function and risk-averse behaviour, indicating that less capital is used than the cost minimising outcome. Ž2. If the optimisation process yields a positive covariance term, the result corresponds to a convex function and risk-loving behaviour of the firm, indicating that the capital᎐labour ratio is above the cost minimising level. Ž3. If the optimisation process yields a zero covariance term, the capital᎐labour ratio equals the cost minimising level, implying that utility is linear and the firm is risk-neutral. It is useful here to clarify the point that the objective function of the firm is to maximise utility, as opposed to the more standard assumption of maximising profits. We choose the former rather than the latter because, under conditions of
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uncertainty, expected utility maximisation is the most convenient simple way to characterise behaviour. It has the advantage of encompassing interesting special cases such as risk-aversion, risk-neutrality, and risk-loving behaviour. Expected profit maximisation is equivalent to risk-neutral behaviour Žsee, e.g. Hirshleifer and Riley, 1992.. Therefore, if the firm’s objective were to maximise expected profits, it would not admit the various special cases which we wish to test.1 Clearly, using expected profit maximisation instead of expected utility maximisation would make a difference to the results of the model, in that it would rule out the interesting cases which we test. This can be seen in Eqs. ŽA8. and ŽA9. in Appendix A. Under profit maximisation U⬙ is, by construction, identically zero, hence so is covŽ l,U⬘..2 The intuition behind the result that risk aversion Žloving. implies a lower Žhigher. capital᎐labour ratio is as follows. Under demand uncertainty, fluctuations in demand lead to fluctuations in output, revenues, costs, and profit. Risk-averse firms choose a capital stock to reduce profit fluctuations. In our model, the volatility of profits depends on how volatile costs are relative to revenues. The volatility of revenues Ždetermined by pu. is exogenous in the model. The capital stock is fixed, and labour is adjusted to keep the firm on the production function, given output, which is set equal to the exogenously fluctuating level of demand. Therefore, the volatility of costs depends on the volatility of labour input. The higher is the capital᎐labour ratio, the less volatile is labour input, for a given capital stock, in relation to output fluctuations. In other words, the smaller is ⭸Ž⭸qr⭸l .r⭸Ž krl .. This can be seen from Eq. ŽA5. in Appendix A, or more directly by differentiating the production function, or by drawing the usual isoquants in capital᎐labour space. Thus, the higher is the capital᎐labour ratio, the more volatile are profits, because costs become increasingly less volatile than revenues. Paradoxically therefore, risk-averse firms in our model will wish to have more volatile costs Žthrough a lower capital᎐labour ratio., as this will mean that costs move more in line with revenues, thus leading to less volatility in profits. Clearly, if firms are risk-averse and the capital᎐labour ratio is less than the cost-minimising outcome, then, as output, labour input, costs, and profits increase, the marginal utility of profits decreases as the risk-averse firm moves up the profit function, giving a negative covariance between marginal utility and labour input. If firms are risk-lovers, then the marginal utility of profit will increase as firms move up the profit function as output rises, giving a positive covariance between marginal utility and labour input. This is the link we exploit to generate testable hypotheses. The testable proposition of the model, therefore, is that risk-loving firms faced with demand uncertainty prefer to have high capital᎐labour ratios, whereas risk-
1
It may be argued that even with profit maximizing it is possible to account for risk-aversion by not using a constant discount rate in a dynamic optimisation framework. However, this is not the case in this paper as our model has static optimisation. 2 This point is emphasised in Appendix A in the paragraph following Eq. ŽA10..
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averse firms prefer low capital᎐labour ratios. To represent this proposition, a corresponding empirical equation can be specified as follows: Ž KrL . i s  0 "  1UNC i q  2Wi q i
Ž1.
where UNC denotes a measure of demand uncertainty; W is the cost per employee and serves as a control variable in estimation and testing; is an error term; and i s 1, 2, 3,....., n firms. The sign of  1 is theoretically dichotomous Ž". and captures the competing hypotheses on the risk behaviour of firms: 3 if the firms are risk-lovers, the sign is positive; a negative sign indicates risk-averse firms.
3. The dataset and empirical procedures We use the dataset AMADEUS which contains firm level financial data for more than 200 000 non-financial firms in Europe, including Poland. The data are provided by local bureaus and AMADEUS compiles the dataset by only including companies which satisfy one of the following size criteria. 1. a turnover greater than 10 million Euro; 2. a number of employees greater than 150; 3. a total asset sum greater than 10 million Euro. Uniformity is achieved by standardisation of accounting information. A big advantage of the dataset is that it comprises listed as well as unlisted firms. For Poland, where there is only a small amount of firms listed on the stock market, this implies that many more firms can be taken into account in the analysis. However, the fact that the dataset focuses on unlisted firms is for some types of analysis also a drawback. For example, the dataset does not contain market values; this limitation precludes analyses for which Tobin’s Q is required. Another drawback of this dataset is that, for most countries including Poland, it only contains yearly data for 1992᎐1996. Obviously, this is a main shortcoming. But it should be taken into account that for most eastern European countries, given the structural break associated with the collapse of the communist system, it would be questionable whether data for the pre-1990 period, if they were available, could be useful at all. Due to the limited time period for which the data are available, our analysis is based on mean values for the 1992᎐1996 period. We calculate the mean value for all original variables prior to any other transformation. By using means, cyclical 3 In some recent studies, the theoretical dichotomy is not addressed. For example, Ghosal Ž1991. postulates only a negative relationship between demand uncertainty and the capital᎐labour ratio. However, unlike this paper, the model by Ghosal Ž1991. is specified as Ž KrL. s f ŽUNC, SIZE. where SIZE is a measure of firm size, and serves to counteract the negative relationship between demand uncertainty and the capital᎐labour ratio.
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influences are avoided.4 All data are denominated in millions of current US dollars. Since US inflation has been moderate in the sample period, this transformation helps to avoid inflation problems. The calculation of one important variable in the analysis ᎏ demand uncertainty ᎏ requires time series data. In order to be able to make a reasonable estimate of demand uncertainty, we only consider firms for which at least 4 years of the relevant data are available. Moreover, we balance the data set so that in all regressions the same amount of firms are taken into account. Finally, to account for extreme outliers, we delete from all variables used in the estimates the five lowest and the five highest values. The final dataset contains 148 Polish firms. In order to determine the most appropriate method for measuring uncertainty, we consider the following methods used in the literature Žsee Lensink et al., 2001.: 1. 2. 3. 4. 5.
the S.D. of the unpredictable part of a stochastic process; the S.D. of the variable under consideration; the S.D. from a geometric Brownian process; the S.D. derived from survey data; the Generalised Auto-Regressive Conditional Heteroskedastic ŽGARCH. model of volatility.
In this paper uncertainty is measured using the first rather than the second method in order to represent a stochastic process. We do not use the third method because the derivation of uncertainty from a geometric Brownian process requires continuous data, which we do not have for this study. We also rule out the fourth method because this paper does not use survey data. We could have used a GARCH model to forecast uncertainty. However, the GARCH model is especially relevant for high frequency data which display clustering effects, whereas the data set used in this paper contains annual observations.5 Specifically, the approach we use to construct the demand uncertainty variable consists of two steps. In the first step, we formulate and estimate a forecasting equation which determines the expected part of the variable under consideration. In line with many other empirical studies Žsee, among others, Aizenman and Marion, 1993; Ghosal, 1995; Ghosal and Loungani, 1996., we use an autoregressive process as the forecasting equation. Due to the limited time series data available
4
By avoiding cyclical fluctuations, we are able to focus on the underlying nature of the relationship between demand uncertainty and the capital᎐labour ratio. Moreover, the period of 1992᎐1996, for which the data are available, is not characterised by any important cyclical downturns in Poland or the rest of the world economy. 5 The data period is too short to allow directly for time variation in uncertainty. Moreover, since we estimate the model as a single cross-section it is not possible to allow for time-varying uncertainty. The measure of uncertainty we use is the S.D. of the difference between the actuals and the conditional forecast, and therefore does allow for information in the dataset, but only to be updated outside the timespan of the dataset and not within it.
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per firm, we use a first-order autoregressive process. We consider two alternative specifications, with and without a constant. lnSAL t s a1 lnSAL ty1 q e t
Ž2.
lnSAL t s a2 q a3 lnSAL ty1 q t
Ž3.
where SAL denotes sales of firms; a1 and a3 are the autoregressive parameters; a2 is a constant; ln denotes natural logarithm; and e t and t are error terms.6 We estimate the above given equations for all firms in our dataset. In the second step of our procedure, the proxies for demand uncertainty are obtained by calculating the S.D. of the unexpected part of SAL, i.e. the residuals from the forecasting Eqs. Ž2. and Ž3., respectively. Hence, UNC1 refers to the proxy obtained from the equation with a constant, while UNC refers to the proxy obtained from the equation without a constant.7 In all, therefore, we initially extract the following set of variables from the dataset: K, tangible assets; L, the number of employees; CEMPL, the total cost of employees; and SAL, sales. Basing on these variables, we construct the variables used in the estimates, namely CLRAT, the capital᎐labour ratio, calculated as KrL; W, cost per employee, calculated as CEMPLrL and used as a control variable in the estimates; and UNC and UNC1 which denote the proxies for demand uncertainty, calculated by using SAL, as explained above. In Table 1 we give some statistical moments of the main variables used in estimating the model. The first and second moments Žmean and S.D.. suggest fairly good dispersion characteristics for a cross-section sample of 148 Polish firms. For example, the capital᎐labour ratio ranges from a minimum of 0.0012 to a maximum of 0.064 with a mean value of 0.013 and a S.D. of 0.0107; demand uncertainty measured without a constant ranges from 0.85 to a maximum of 78.62 with a mean value of 9.64 and a S.D. of 12.19; the measure of demand uncertainty that includes a constant range from 0.31 to 50.51 with a mean value of 5.96 and S.D. of 8.23; wages range from 0.0025 to 0.0107 with a mean of 0.0044 and a S.D. of 0.0014. The results thus show a wide diversity in the indicators, suggesting extreme outliers in the data are unlikely.
4. Estimation results Although the economic theory discussed in Section 2 yields hypotheses on the effect of demand uncertainty and wages on the capital᎐labour ratio, the economic 6 Given the short span of the data for each firm Žfive annual observations., we did not experiment with more complicated specifications of the autoregressive process, e.g. fitting a linear trend or specifying a quadratic function to capture the fluctuations of sales around the trend. 7 As observed by Aizenman and Marion Ž1993. the main shortcoming of this method is that future changes in the environment that are fully anticipated are still treated as uncertain.
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Table 1 Statistical moments of the main variables used in model estimation a
UNC UNC1 CLRAT W
Mean
Median
S.D.
Max
Min
9.64 5.96 0.013 0.0044
5.96 3.55 0.010 0.0039
12.19 8.23 0.0107 0.0014
78.62 50.51 0.064 0.0107
0.85 0.31 0.0012 0.0025
a UNC denotes the proxy for demand uncertainty obtained by estimating the sales ŽSAL. equation without a constant; UNC1 refers to the proxy for demand uncertainty obtained by estimating the SAL equation with a constant; CLRAT is the capital᎐labour ratio, calculated as KrL, where K is tangible assets and L is the number of employees; W is cost per employee, calculated as CEMPLrL, where CEMPL is the total cost of employees.
functional relationship is not completely specified. We therefore empirically explore the exact form of the estimating equation by experimenting with variants of Eq. Ž1. in linear, quasi-logarithmic and logarithmic forms. In all cases, however, we do not find it plausible to include in the model an interaction between demand uncertainty and wages as an additional argument in Eq. Ž1., i.e.  3 ŽUNC = W . i . Table 2 presents the estimation and testing results. The results show that the fit of the equations is generally good in terms of the F-statistic; the adjusted R 2 is low, which is typical of cross-section regressions of this type. Given that heteroskedasticity could be important across firms, the t-values were computed from White’s heteroskedasticity-consistent standard errors ŽWhite, 1980.; the t-statistics show that all the coefficients are significant. The evidence is unambiguous. Irrespective of whether the empirical model is specified in its linear, quasi-logarithmic, or logarithmic variants, the evidence shows that there exists a significant positive relationship between demand uncertainty and the capital᎐labour ratio. In this specific study, therefore, the evidence resolves the theoretical dichotomy in Eq. Ž1. by showing that with respect to a sample of 148 firms in the transition economy of Poland, the sign of  1 is empirically positive, with statistically significant coefficients. The evidence is therefore unequivocal that Polish firms are risk-lovers; with an increase in demand uncertainty, the firms tend to increase their capital᎐labour ratio. The logarithmic variants of the model offer additional information about the elasticities. For model variant Ž5. in Table 2, when demand uncertainty is measured on the basis of Eq. Ž2. without a constant in the first-order autoregressive process, it is shown that, on average, a 1% rise in demand uncertainty will result in a 0.22% increase in the capital᎐labour ratio. The elasticity falls to 0.15 when demand uncertainty is measured on the basis of Eq. Ž2., without a constant in the first-order autoregressive process, as in model variant Ž6..8 The F-test statistic indicates that model variant Ž5. outperforms the rest of the model variants considered in this 8
In general, however, the results reported in Table 2 are reliably robust to the specific measure of demand uncertainty, i.e. UNC or UNC1.
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Table 2 Estimation results for a cross-section sample of 148 Polish firms a Ž1. CLRAT UNC
Ž2. CLRAT
0.00020 Ž2.15.
UNC1
Ž3. LnŽCLRAT.
Ž4. LnŽCLRAT.
Ž5. LnŽCLRAT.
0.0247 Ž3.12. 0.00026 Ž2.01.
0.0188 Ž3.06.
lnŽUNC.
0.2288 Ž3.50.
lnŽUNC1.
W
0.1578 Ž2.81. 1.137 Ž1.91.
1.198 Ž1.95.
94.11 Ž2.34.
98.49 Ž2.37.
lnŽW .
Constant
F Adj. R2
Ž6. LnŽCLRAT.
0.0064 Ž2.40.
0.0066 Ž2.35.
y5.14 Žy27.34.
y5.13 Žy26.30.
0.531 Ž2.72.
0.614 Ž3.00.
y2.09 Žy1.92.
y1.04 Žy1.27.
6.37
5.24
7.59
6.40
11.26
8.39
0.07
0.06
0.08
0.07
0.12
0.09
a
t-values are between parentheses, below the estimated coefficients; the values are based on White’s heteroskedasticity consistent standard errors. Eqs. Ž1᎐6. in the table are variants of the following generic model: CLRATi,t s  0 " 1 UNC i,t q  2 Wi,t q i,t , where CLARET is the capital᎐labour ratio; UNC is a measure of demand uncertainty; W is a labour cost variable; subscripts i,t denote firm i at time t; and is the error term.
study. As theoretically postulated in Sections 2 and 3, the labour costs variable is empirically positive for all model variants explored in this paper. In general, our empirical results can be interpreted to mean that firms in Poland are risk-lovers with respect to profits rather than labour costs. In other words, the firms respond to demand uncertainty by increasing their capital᎐labour ratio because they are more concerned to have stable labour costs than they are to have stable profits. This seems to us to be a reasonable finding in the context of Poland, and eastern Europe transition economies in general, given that the labour force, which were previously employed in government-owned corporations, have the wrong skills and are costly to retrain to be able to work productively for private sector companies in a market economy; there are also high ancillary costs of hiring and firing, among other labour costs.
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An alternative explanation of our results takes us back to the theory underpinning our empirical equation, and relates to the flexibility of labour relative to capital. If labour can adjust to price shocks, fluctuations lead the firm to change the labor᎐capital ratio, so the marginal product of capital changes more than price changes. Hence, in making their input choices following an increase in demand uncertainty, Polish firms tend to increase their capital᎐labour ratio.
5. Summary and conclusion This paper investigates the effect of demand uncertainty on the capital᎐labour ratio of non-financial firms in Poland. A generic model is used to characterise a representative firm operating under imperfect competition and uncertain demand conditions in a transition economy. The main testable proposition of the model is that if firms are risk-lovers, an increase in demand uncertainty increases the capital᎐labour ratio, whereas the capital᎐labour ratio decreases if firms are risk-averse. Using cross-section data for 148 non-financial Polish firms, we test variants of the model in order to estimate the elasticity of the capital᎐labour ratio with respect to demand uncertainty. The econometric results resolve the theoretical dichotomy and show that an increase in demand uncertainty leads to an increase in the capital᎐labour ratio of Polish firms. This finding suggests that Polish firms are risk-lovers. In general, the results are robust to the functional form of model specification Žlinear, semi-logarithmic and logarithmic. as well as the measure of demand uncertainty. The knowledge of how firms react to uncertain demand conditions, uncovered in this study, offers a number of policy implications for Poland and some other transition economies. First, the evidence may have important consequences for the needed set of regulations and corporate governance in Poland as part of the necessary economic management during the transition period. Regulatory policy may be applied to influence the level of demand uncertainty as well as factor demand and factor productivity. For example, an introduction of a sales tax is expected to operate directly on the sales volume of firms and hence on the fluctuation of actual sales, and indirectly on the unexpected component of the sales Žor demand uncertainty.. Since demand uncertainty directly affects the capital᎐labour ratio, the effect may work its way to the demand for, and the pricing of, the firm’s factor inputs such as capital and labour Že.g. employment by firms. and the productivity of these factors. Second, the results shed light on how risk-loving firms behave in the face of a downturn in business confidence. The measure of demand uncertainty used in this paper derives from the unpredictable component of sales by firms and can therefore serve as a useful indicator of business confidence and consumer optimism. An increase in demand uncertainty reduces business confidence but risk-loving firms tend to increase their capital᎐labour ratio. Third, the results imply that, under demand uncertainty, firm managers need to be cautious in making their input policies as these choices
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influence firm output, costs, revenues and profitability. For example, under demand uncertainty, risk-averse firms choose a capital stock to reduce fluctuations in output, revenues, costs, and profit. But because the capital stock is fixed in the short run in the sense that transition economies are characterised by the existence of a part of capital stock which is unproductive, labour is adjusted to keep the firm on the production function. Therefore, managers need to address the volatility of labour inputs in order to come to grips with the volatility of costs; precisely, in making policies about the firms’ factor proportions, managers should be concerned more about fluctuations in labour costs than fluctuations in profits. A fourth important policy implication of our empirical result is that the risk-taking behaviour of firms in Poland with respect to higher capital᎐labour ratios following an increase in demand uncertainty may well be stimulated by the weak legal framework for enforcing financial discipline by firms, and the inability of government to implement insolvency and bankruptcy codes. However, to be able to refine the regulatory and other implications of this study, further and more detailed econometric research is necessary.
Acknowledgements We thank the editor ŽJavier Estrada. and an anonymous referee of this journal for useful comments on a previous version of the paper; constructive criticisms were also received from seminar participants at the University of Birmingham, Tallinn Technical University, University of Economics at Poznan and University of Groningen. We gratefully acknowledge financial support from the European Commission’s PharerACE Programme 1997᎐1999 under Contract Number 966152-R. At the time of writing this paper, Robert Lensink was on sabbatical at Loughborough University. However, the interpretations and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the European Commission.
Appendix A. An eclectic theoretical model
We use an eclectic model based on Holthausen Ž1976.. However, our model differs from the one by Holthausen Ž1976. in the sense that, for reasons of convenience, we specify the main equations which capture the business environment in emerging market economies. The model assumes that the firm is an imperfect competitor who behaves as a price setter. The firm operates in a world where demand is uncertain. Its demand function is therefore specified as: q s u y p
Ž A1.
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where q is total demand, u is a random term and p is the price level. The firm produces goods with a standard neoclassical Cobb᎐Douglas production function with two inputs, capital Ž k . and labour Ž l .: q s k ␣ l Ž 1y␣ .
Ž A2.
A crucial assumption is that labour is completely variable, whereas capital is quasi-fixed. This is taken into account by assuming that capital input is chosen before actual demand is observed, whereas the demand for labour takes place after demand is observed. If the capital stock is set, and actual demand is observed, labour input will always be chosen so that any level of demand is satisfied. To simplify the analysis, labour demand is not considered as a decision variable, but is taken into account implicitly by a labour-requirements function, which is specified as: l s Ž qrk ␣ .Ž 1r1y␣ .
Ž A3.
The firm optimises the expected utility of profit Ž ⌸ .: Max Ž p ,k . EU Ž ⌸ .
Ž A4.
where U is a von Neumann᎐Morgenstein utility function, and E is an expectations operator. The decision variables are the price level and the capital stock. Since the firm is assumed to optimise the utility of profits, we are able to consider the implications of different assumptions regarding the risk behaviour of a firm and the consequences for the capital᎐output ratio. We use Von Neumann Morgenstern utility function. Such a utility function satisfies several conditions. The first is that there is non-satiation, which means that more is preferred over less. Mathematically, this implies a positive first derivative. A strictly concave Von Neumann Morgenstern utility function Žnegative second derivative. implies risk aversion. Risk-loving behaviour implies a convex utility function Žpositive second derivative. and risk-neutral behaviour corresponds to a linear utility function Žsecond derivative is zero.. If we had assumed that firms maximise profits, the analysis would be restricted to a risk-neutral firm. Profit is defined as: ⌸ s p Ž u y p . y w wŽ u y p . rk ␣ xŽ 1r1y␣ . y ic Ž k .
Ž A5.
where w is the wage rate; c is the price of capital; and i is the cost of capital. The demand function and the labour requirement functions are taken into account. It is assumed that w, c, i are given for the firm. The first-order condition with respect to k is:
½
E U⬘ Ž ⌸ . w
ž
␣
l
1y␣ k
y ic
/5
s0
Ž A6.
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For two random variables, EŽ X,Y . s EŽ X . EŽ Y . q covŽ X,Y ., so that the above equation can be rewritten as: EU⬘ Ž ⌸ . E w
ž
␣
l
1y␣ k
y ic q w
/
␣ 1y␣
cov
ž
l k
,U⬘ Ž ⌸ . s 0
/
Ž A7.
so that: 1 E
l k
s
1 y ␣ ic ␣
w
y
k
cov Ž l,U⬘ Ž ⌸ .. Ž A8.
EU⬘ Ž ⌸ .
It follows that, on the one hand, the demand for capital is greater than the amount of capital the firm uses if expected costs are minimised for a given level of output in the case where the left-hand side of the above expression is smaller than icrw. Since, U⬘Ž ⌸ . ) 0, a firm will demand more capital than the cost minimising amount when the covariance term is positive. On the other hand, in the case where the left-hand side of the expression in Eq. ŽA8. is greater than icrw, the demand for capital is smaller than the amount of capital the firm uses if expected costs are minimised for a given level of output. Since, U⬘Ž ⌸ . ) 0, a firm will demand less capital than the cost minimising amount when the covariance term is negative. The crucial indicator, therefore, is the sign of the covariance term. The covariance term can be signed by considering the effects of an increase in the random term. Hence: ⭸U⬘ Ž ⌸ . ⭸u
s
⭸⌸ ⭸u
U⬙ Ž ⌸ . s p y w
⭸L Ž q,k .
⭸q Ž p,u .
⭸q
⭸u
U⬙ Ž ⌸ .
Ž A9.
The first term in brackets on the right-hand side of the above equation is always positive under the assumption that the price is higher than the marginal cost of production. Since qu ) 0, the sign of ␦U⬘Ž ⌸ .r␦u depends on the sign of U⬙ Ž ⌸ .. It follows that: ⭸l ⭸u
s
⭸l ⭸q ⭸q ⭸u
s
1
q
ž /
1 y ␣ k␣
␣r Ž1y␣ .
ky␣ G 0
Ž A10.
Hence, the covariance depends on U⬙ Ž ⌸ .. If U⬙ Ž ⌸ . - 0, which corresponds to a concave function and risk-averse behaviour, the covariance term is negative. In that case less capital is used than the cost-minimising outcome. If U⬙ Ž ⌸ . ) 0, and hence the firm is risk-loving, the covariance term is positive, and accordingly the capital labour ratio is above the cost-minimising level. If U⬙ Ž ⌸ . s 0, U⬘Ž ⌸ . s constant, implying that utility is linear, the firm is risk-neutral; in that case the
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covariance term equals zero, so that the capital᎐labour ratio equals the cost-minimizing level.9
References Aizenman, J., Marion, N.P., 1993. Macroeconomic uncertainty and private investment. Econ. Lett. 41, 207᎐210. Ghosal, V., 1991. Demand uncertainty and the capital᎐labor ratio: evidence from the US manufacturing sector. Rev. Econ. Stat. 73, 157᎐160. Ghosal, V., 1995. Input choices under price uncertainty. Econ. Inq. 33, 142᎐158. Ghosal, V., Loungani, P., 1996. Product market competition and the impact of price uncertainty on investment: some evidence from US manufacturing industries. J. Ind. Econ. 44, 217᎐228. Hirshleifer, J., Riley, J.G., 1992. The Analytics of Uncertainty and Information. Cambridge University Press, Cambridge. Holthausen, D.M., 1976. Input choices and uncertain demand. Am. Econ. Rev. 66, 94᎐103. Lensink, R., Bo, H., Sterken, E., 2001. Investment, Capital Market Imperfections and Uncertainty. Edward Elgar Žforthcoming., Cheltenham. White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817᎐838. 9 Note that strictly speaking the above theoretical analysis gives the o¨ erall impact, rather than the marginal impact, of uncertainty on the capital᎐labour ratio. However, the empirical Eq. Ž1. and the regressions in the empirical part of the paper reflect the marginal Žpartial. effects of demand uncertainty on the capital᎐labour ratio.