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Information on the entrepreneur's subjective probability distribution over future demand for the firm's products is used to construct the expected variance of demand, which is used as a measure of uncertainty. Empirical results support the prediction that firms wait to invest until the marginal revenue product of capital reaches a firm-specific hurdle level.
Moreover, higher uncertainty raises the hurdle level that triggers investment, and uncertainty has a negative effect on investment levels that is greater for firms with more irreversible investment. This is a preview of subscription content, log in to check access.
Rent this article via DeepDyve. Reprints and Permissions. Pattillo, C. Investment, Uncertainty, and Irreversibility in Ghana. IMF Econ Rev 45, — Download citation. For one year ahead demand expectations, the next largest fraction of firms had a coefficient of variation in the range 9—11 percent. The three year ahead expectations indicate that there was more uncertainty over this longer horizon, but only by a small amount.
Both frequency distributions indicate that this measure of uncertainty exhibits substantial variation across the firms in the sample. Although interest centers on the uncertainty variable, it is also important to control for the expected mean demand growth. The growth rate of demand enters into the investment trigger condition in the investment under uncertainty model. Moreover, the estimated effects of the uncertainty variable would be biased if the expected mean demand growth is not controlled for.
The bottom panel of Table 4 displays one and three year ahead expected demand growth. Most firms are optimistic: the largest proportion expect demand growth of more than 30 percent. Guiso and Parigi and Pattillo classify firms as having more easily reversible investment if the firm either leased capital goods, bought used capital goods or sold capital.
One weakness of this proxy is that it cannot distinguish firms with more irreversible investment from those that to date have optimally never chosen to lease, buy used or sell capital. In this paper, the irreversibility proxy used is the ratio of the real sales value of the capital stock to its real replacement value. A dummy variable is constructed for use in the regressions. This measure of irreversibility is related to constructs used in the theoretical literature.
Partial irreversibility may also be related to the presence of industry-specific capital. When a firm attempts to sell capital goods because of poor market conditions it may find few buyers or low prices from other firms in the industry which face the same market conditions.
The average ratio of sales to replacement value of the capital stock differs across manufacturing sectors in Ghana, but there is also substantial firm-level variation within a sector. From Appendix 1 equation A. The capital share is derived by estimating a Cobb-Douglass production function. Before turning to the results from the method outlined above, some preliminary regressions will be discussed.
A comparison could examine the effect of the uncertainty variable in OLS regressions of the investment rate for firms with positive investment. This would be a misspecification, however, since it would not allow for the selectivity in including only firms with positive investment.
Preliminary discussion, therefore, will be based on the estimation of a Tobit investment model, shown in Table 5. Interest rates are not very relevant given that only approximately one-quarter of the firms obtain bank financing for their capital expenditures. If estimation were based on a longer panel, firm-specific costs of capital might be controlled for using a fixed-effects model.
However, that method cannot be pursued in the current inquiry. The profit rate is included as a measure of internal liquidity, and its significance is interpreted as evidence that financing is important for investment decisions. For the overall sample, the Tobit equation indicates that investment is positively related to the change in real value-added, the profit rate, the MRPK and the size of the firm, and negatively related to the age of the firm.
The equations control for industrial sector not reported. The variance of expected demand is not significant, however, for the sample of all firms. Column 2 considers the effect of reversibility on this relationship. When the reversibility indicator is interacted with the variance of expected demand, the results indicate that most of the negative effect of uncertainty comes from irreversibility.
Moreover, the t-statistic on the interaction term indicates that there is a significant difference between the slopes for firms with reversible investment and firms with irreversible investment. Although these results are encouraging, the Tobit specification forces regressors to have the same effect on the probability of investing and on the investment level.
Therefore it cannot be used to test the prediction that investment is triggered when the MRPK reaches a particular hurdle level. The restriction that regressors have the same effect on the probability of an observation being a non-limit observation and on the level of that variable is testable. Using a likelihood ratio statistic as in Greene , pg.
The first set of results do not make allowance for firms with differing ability to reverse their investment expenditures. Two issues can be explored: i is investment triggered when the MRPK reaches a hurdle level? The analysis will also consider the effect of uncertainty on the investment level, for firms with positive investment.
Table 6 presents the estimates. All estimates control for industrial sector. The reduced form probit results show that faster growth rates of value-added, higher profitability, and higher MRPK increase the probability of investing. Larger firms are more likely to invest and older firms less likely. The expectation of higher mean demand growth is not significant in the decision to invest.
High variance of expected demand, however, does lower the probability of investing. The model correctly predicts 68 percent of the observations. Next I assume that for firms with positive investment the MRPK has reached the investment trigger, implying that the MRPK of investing firms can be used as a first-stage proxy for the trigger.
Column 2 shows how the expected mean and variance of demand and the profit rate affect the firm-specific hurdle level of the MRPK that triggers investment. Many of the other variables are not significant. The results do indicate, however, that the variance of expected demand has a positive and significant effect on the hurdle level of the MRPK for firms with positive investment. This result is interpreted as support for the prediction that high levels of uncertainty increase the investment trigger.
To support the interpretation that the MRPK for investing firms can be viewed as a preliminary proxy for the investment trigger, the MRPK equation is also estimated using the full sample of investing and non-investing firms. This would not represent the investment trigger and there is no clear prediction on the effect of uncertainty. For firms that are not investing, the MRPK is not equal to the trigger. The next step is to estimate the structural probit equation 7 that follows from the irreversible investment model.
A variable representing the investment trigger for all firms is required. As indicated in equation 8 , the coefficients from column 2 and the firm values for the determinants of the trigger can be used to create a predicted trigger for firms with positive and zero investment. The trigger has been constructed as a function of the firm-level measure of uncertainty. The growth rate of value-added and profitability are also found to have a positive and significant impact on the decision to invest.
In order to account for the use of predicted values as regressors, the standard errors are corrected using the method of Murphy and Topel Finally, column 5 presents the estimates of equation 9 , the investment rate, conditioned on positive investment. The model includes the same selection-bias correction as used in the trigger equation.
Growth of value-added and profitability are positively related to the investment level. Firm size and age were determinants of the decision to invest, but they do not influence the investment level, conditional on positive investment. While higher expected mean demand growth did not affect the probability of investing, it does have a positive and significant effect on investment levels. The variance of expected demand, or the uncertainty proxy, has a negative and significant effect on the investment level.
Next we turn to examination of the following issues: i do the results still indicate that firms invest when the MRPK reaches a hurdle level when different degrees of reversibility are accounted for; ii does uncertainty increase the investment trigger to a greater extent for firms with irreversible investment; and iii does uncertainty have a larger negative effect on the investment levels of firms with more irreversible investment.
The method followed is the same as that above. In Table 7 , the reduced form probit model provides strong evidence that for the decision to invest, uncertainty is a greater deterrent for firms with irreversible investment than for firms with more easily reversible investment.
For firms with more irreversible investment the effect of uncertainty is negative and significant, while the coefficient is insignificantly different from zero for firms with more easily reversible investment. There is a significant difference between the slopes for firms with reversible investment and firms with irreversible investment.
Column 2 again examines the effect of the expected mean and variance of expected demand and the profit rate on the hurdle level of the MRPK that triggers investment. The impact of reversibility on the relationship between uncertainty and the investment trigger is not completely clear cut. Higher uncertainty increases the investment trigger for firms with both irreversible and reversible investment, but the difference in the coefficients is not statistically significant.
However, I cannot reject that the coefficient on uncertainty for firms with more reversible investment is insignificantly different from zero. It seems that uncertainty increases the MRPK that triggers investment, but there is only weak evidence that this effect is stronger for firms with more irreversible investment. The form of the structural probit model in column 3 is the same as the one in Table 6.
Is there still support for the prediction that investment is triggered when the MRPK reaches a firm-specific hurdle level? Accounting for the degree of reversibility when predicting the trigger strengthens the results—the size of the coefficient on the deviation between the MRPK and the trigger is much larger and has increased in significance.
Turning to the final issue, recall that the theoretical models did not yield clear cut results on how reversibility influenced the investment-uncertainty relationship. Selection-bias corrected estimates of the investment rate are shown in column 4. The estimates indicate that for this sample of Ghanaian manufacturing firms, uncertainty has a greater negative effect on investment rates for firms with more irreversible investment.
While the coefficient for firms with more easily reversible investment is still negative, it is much smaller, and there is a significant difference in the coefficients for firms with irreversible and reversible investment. Several different variants of the above specification were estimated in order to assess the robustness of the results. First, a variable representing total firm debt was added to the probit equations for the decision to invest and the investment level regressions.
Highly leveraged firms may face higher borrowing costs which would effect their cost of capital and investment decisions. The results were qualitatively the same and debt was significant in the investment level, but not the probability of investment equations. Second, two different variants of the uncertainty measure were calculated. In one, the conditional variance from the survey questions is combined with base year value-added, rather than sales, to calculate the expected variance of demand.
In another the variance of expected demand is scaled by output, rather than the capital stock. In both cases the results were broadly similar. Third, identification by excluding age and size from the investment rate equation, rather than the MRPK, was tried and also did not significantly effect the results. The results in Table 8 illustrate that the only significant change in the results is in the reduced form probit model of the decision to invest.
Although the results are generally supportive of the hypotheses, there is scope for improvement in the analysis. First, an alternative to the probit method could be found in order to satisfy the model prediction that capital is purchased to prevent the MRPK from rising above the optimally derived trigger.
Finally, although it has been argued that the selection model is appropriate, the selection bias terms were not significant. The reduced form model should be extended to further explore the selection mechanism. Firm level panel data are extremely useful for exploring the effect of uncertainty on investment. Theoretical models show that firms that cannot easily reverse investment decisions wait to invest until the MRPK reaches a specific hurdle level.
The hurdle level is an increasing function of uncertainty. Although one would expect uncertainty to increase the trigger to a greater extent for firms with more irreversible investment, there are no theoretical predictions on this issue. There is also some controversy in the theoretical literature on the impact of uncertainty and irreversibility on average investment levels. Firm level data can be used to test these issues.
This facilitated estimating the impact of the variance of expected demand, while also controlling for the expected mean demand growth. The empirical results provide support for the prediction that firms wait to invest until the MRPK reaches a firm-specific hurdle level. It is also found that greater uncertainty leads to an increase in a first-stage proxy for the investment trigger, although there is only weak evidence that this effect is stronger for firms with more irreversible investment.
The results indicate that uncertainty has a negative effect on investment levels and that the effect is significantly greater for firms with more irreversible investment. L is labor, a perfectly flexible production factor that can be rented at the rate w t. A t is a technological progress indicator. Operating profits can be written in reduced form as:. Z t depends positively on the strength of demand and on productivity, and negatively on the cost of factors other than capital.
This question tries to ascertain by how much you expect the output of your firm to change, in terms of volume of products? This is done with the help of the table below. The table specifies various ranges by which output may change. This is shown in the first column, for example, an increase in output of 20—30 percent, a decrease in output by more than 30 percent, etc. Now we need you to estimate the likelihood of each expected change in output occurring on a scale of 0— Remember there are nine categories and your total points should add up to Wolfe ed.
Greene William H. Ferderer J. Guiso Luigi T. Japelli and D. Hartman R. Collier and C. Pattillo eds. Rosen Sherwin and Robert J. White H. All remaining errors are my own. Profit rates vary widely across size classes, and the mean and median rates decrease with firm size. Bigsten et. The model can be generalized to include other flexible factors of production in addition to labor. If so, the price of all flexible factors is assumed to be stochastic. Since the data are aggregated over a year, and measurement errors are present, we may assume that the condition preventing the MRPK from ever rising above the trigger will not always hold empirically.
However, the fact that the probit specification does not strictly match the model condition is a weakness of the approach. The objective was to interview the individual who made investment and production decisions. Firm owners were interviewed in the cases of sole proprietorships, partnerships and limited liability enterprises, and managing directors for larger corporations or multinational subsidiaries. The idea for the survey question and the variable comes from Guiso, Jappelli and Terlizzese , who use a related measure to test for precautionary savings by households.
More recently, Guiso and Parigi have also used such a measure to study investment in a cross-section of Italian firms. Note that reported coefficients are the marginal effects evaluated at the mean for all observations. User Account. IMF eLibrary. Advanced search Help. Kitts and Nevis St. Lucia St. Public Health Health Policy. Print Citation Alert off.
Information on the entrepreneur's subjective probability distribution over future demand for the firm's products is used to construct the expected variance of demand, which is used as a measure of uncertainty. Empirical results support the prediction that firms wait to invest until the marginal revenue product of capital reaches a firm-specific hurdle level. Moreover, higher uncertainty raises the hurdle level that triggers investment, and uncertainty has a negative effect on investment levels that is greater for firms with more irreversible investment.
Introduction II. Background III. The Sample IV. Theory A. Literature B. Determining the Investment Trigger Point V. Econometric Method VI. Variable Definitions A. Uncertainty Variable B. Irreversibility Proxies C. Results A. Tobit Model B. Sample Selection Model C. Show Summary Details I. I ntroduction This paper analyzes the impact of uncertainty on the investment behavior of Ghanaian manufacturing firms using a panel data set for the years — B ackground Since the beginning of the ERP in , uncertainty has dampened private investment, although the nature of the uncertainties have changed over the period.
T he S ample The Ghana survey has collected panel data for the five years —95 from a sample of approximately manufacturing firms. Table 1. Table 2. Table 3. November 6, Notes: 1 Mi is the Ith the percentile, N is the number of observations. Literature A firm that cannot reverse its investment decisions faces a higher user cost of capital than a firm with perfectly reversible investment, and this leads to lower investment for firms with irreversible investment.
Determining the Investment Trigger Point Since one issue of interest is how irreversibility influences the effect of uncertainty on investment, the most appropriate model to consider would include parameterization of the degree of reversibility.
E conometric M ethod Condition 1 above can be written as:. V ariable D efinitions Estimated equations for the decision to invest, and the MRPK and the investment level, conditional on positive investment will be presented. Table 4. Irreversibility Proxies Guiso and Parigi and Pattillo classify firms as having more easily reversible investment if the firm either leased capital goods, bought used capital goods or sold capital.
R esults A.