THE EFFECTS OF MACROECONOMIC UNCERTAINTY ON IRREVERSIBLE INVESTMENT
A Dissertation
Trang 2UMI Number: 3107363 Copyright 2003 by
Sile, Ayse Esin
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Trang 3Copyright 2003 by Ayse E Sile All Rights Reserved
Trang 4GEORGETOWN UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES This is to certify that we have examined the doctoral dissertation of Ayse E Sile
entitled The Effects of Macroeconomic Uncertainty on Irreversible Investment submitted to the faculty of Economics
in partial fulfillment of the requirements for the degree of Doctor of Philosophy
This dissertation is complete and satisfactory in all respects, and any and all revisions required by the final examining committee have been made
Susan M Collins CH) Cl fo uly 4, 2e2f
Thesis Advisor Date Robert Hussey, S.J Committee Member 7y fo vã : ` ` ˆ ` Arik Levinson db “7 lạ | OSs ee
Director of Graduate Studies ‘ Signatufe
Trang 5THE EFFECTS OF MACROECONOMIC UNCERTAINTY ON IRREVERSIBLE INVESTMENT
Ayse E Sile, M.A
Thesis Advisor: Susan M Collins, Ph.D ABSTRACT
I study the effects of demand and supply uncertainty on irreversible investment My objective is two fold First, I investigate whether demand and supply uncertainty have similar effects on investment Second, I study the effects of sunk costs on investment
I make three contributions to the existing literature First, I develop a methodology for analyzing the effects of demand and supply uncertainty on the level of investment where sunk costs interact with the both uncertainty measures in the estimation equation Second, I explore the extent to which the uncertainty-investment relationship is affected by the magnitude of sunk costs Third, I address the econometric issues raised by estimating a regression equation in which generated regressors appear
I use two different data sets in my empirical analyses In Chapters 1 and 2, I evaluate the effects of industry-wide uncertainty and sunk costs on the level of investment in 2-digit Turkish manufacturing industries In Chapter 3, I investigate how demand and supply uncertainty, and sunk costs affect the aggregate manufacturing investment in thirteen OECD countries
In Chapters 1 and 2, I find that the effect of supply uncertainty on investment is negative in full sample estimates and in the sample of industries where demand is price elastic The estimated impact, however, is positive in the sample of industries where demand is price
Trang 6inelastic I also find that investment is negatively associated both with sunk costs and the demand uncertainty
In Chapter 3, I find that the negative quantitative impact of the demand uncertainty on investment is larger than the impact of the supply uncertainty measure and that the estimated effects of the sunk cost proxy and the demand uncertainty measure are negative and statistically significant The results suggest that the methodology provides quite a good fit for the data on the OECD countries Therefore, the empirical results suggest that interaction variables are key in studying sign of the uncertainty-investment relationship
These empirical findings could have important implications for countries They suggest that policy makers should take into consideration the interactions between different types of uncertainty and sunk costs
Trang 7Acknowledgments
I would like to thank Prof Susan M Collins for her support and guidance throughout this research She was always encouraging and considerate She helped me overcome all the difficulties I had during my research
I am especially grateful to my committee members Their supervision was enjoyable and ultimately informative I would like to thank Prof Robert Hussey, S.J for his diligent efforts in reviewing my work and for his thoughtful comments and suggestions I would also like to thank Prof Arik Levinson for his invaluable comments and for the interesting discussions
Trang 8Dedication
I dedicate this dissertation to my grandmother, Muyesser Turkmenoglu and to my uncle, Osman Turkmenoglu, both of whom passed away during the course of this research I miss you both dearly
Trang 9Table of Contents InfrOdUCfiOT SH TT Ki Đo B n n n BE BE nh tà ng tà tàn tàn ng 8 Chapter 1: The Impact of Macroeconomic Uncertainty and Sunk Costs on Investment 1.1: Introducfion -. ni HH KH nh nh nh nh nh hà hy 2800 2 «e6 aaAaaa es 1.3: Theoretical ffamewWOFK nh nh nh nh ra 1.4: Methodology - cà nh nh nà nh nà Km nh TH bi tà ky 1.5: Data and preliminary sfafISEÍCS ccQQ nn HH nhe nh kia 1.6: Estimation r€SUÏfS - TH SH HT KT Ki kh kh By 1.7: CONCIUSION 00 ccc ecc cece cseeseseeeeseeevsuuesereeuseesrbeeeesaeesereaeeseuesees Chapter 2: Macroeconomic Uncertainty, Sunk Costs, and Investment: Time-Series and Cross-Sectional Evidence From Turkish Manufacturing c cà cv
ĐH ¡c(0)01)19:10).aaaAIaiadđaiaiiaaaaắẳẢẮ
29002 Anh: ‹(aẦ)Ẵ1
2.3: Theoretical model and methodology - chưa 2.4: Data and preliminary sfatiSHICS - HH HH nhe nhe vn 2.5: Estimation r€SUÏfS - cu nu HH TT ng ky nh và
2.6: Conc[UsiOn cuc 0n ng HH HH TK Km TK n nh ky xa Chapter 3: Macroeconomic Uncertainty, Sunk Costs, and Aggregate Investment:
Empirical Evidence From the OECD Countries - c.c 2<:
ESHo co ]Ả
Trang 103.4: Data and preliminary s†afISfICS c HH n HH nh nh 74
K2 oi n eee 81
3.6: Conclusion nọ nọ HH Ki Ki Ki Kế nộ nà ĐK nh vết 84 Appendix A: Data description and sources for Chapter Í - «cà ceằ 95 Appendix B: Computation of coefficients and standard errors for marginal effects in
Tables 5, 7, 11, 13, and 2( cọ HH HT HT HT nà Kì nh HH 98 Appendix C: Theoretical model for Chapfer 2 -. HH nh nhe ha 99 Appendix D: Data description and sources for Chapter 2 - -.- Ặ 2c se 101 Appendix E: Specification tests for the uncertainty measures 1n Chapter 2 103 Appendix F: Correction method for øenerafed T€ĐT€SSOTS uc S snnnnesằ 105 Appendix G: Data description and sources for Chapter 3 cẰc.Ằ 109 Appendix H: Specification tests for the uncertainty measures Chapter 3 111 Appendix I: Summary of existing literature on sunk cOsf DfOX1€S - 112
?100105x0n PA -.1 114
List of Figures
Trang 11and sunk cost measure across sixteen Turkish manufacturing Industrles 30 Table 3: Descriptive statistics on the supply and demand uncertainty measures
across sixteen Turkish manufacturing 1ndusfr1€s .‹ ‹-c-<c-<c< << 31 Table 4: Panel estimation results for the investment-value added ratio as a function of uncerfainfy measures, sunk cost measure, and OufpUt - nền 32 Table 5: Net effects of change in the sunk cost measure, demand uncertainty measure, and supply uncertainty measure on the investmenft-value added ratio - - -‹- 33 Table 6: Panel estimation results for the investment-value added ratio as a function of uncertainty measure, sunk cost measure, and oufput in industry ØTOUDS - 34 Table 7: Net effects of change in the sunk cost measure, demand uncertainty measure, and supply uncertainty measure on the investment-value added ratio in industry groups 35 Table 8: Descriptive statistics on labor productivity and inventory series in sixteen Turkish Manufacturing INGUSUFICS cece cece eee ceca eae e cece ease ncaa eetea seen ee eatateeetee pense ned 57 Table 9: Descriptive statistics on the demand and supply uncertainty measures and
sunk cost mneasure 1n sixteen Turkish manufacturing Industri€s - 38 Table 10: Panel estimation results for the investment-value added ratio as a function of uncertainty measures, sunk cosf me€asure, and Oufpuf ‹ ‹-.‹-c cv s<+2 59
Table 11: Net effects of change in the sunk cost measure, demand uncertainty measure, and supply uncertainty measure on the investment-value added ratio -.-.- - 60 Table 12: Panel estimation results for the investment-value added ratio as a function of the uncertainty measures, sunk cost measure, and outpuf 1n Industry øT0ups 61 Table 13: Net effects of change in the sunk cost measure, demand uncertainty measure, and supply uncertainty measure on the investment-value added ratio in elastic and inelastic industry
Trang 12Table 14: Aggregate empirical studies on the sign of uncertainty-investment relationship 87 Table 15: Disaggregate empirical studies on the sign of uncertainty-investment relationship 89 Table 16: Effects of different uncertainty measures that are used in empirical analyses in Tables
lun PE 90
Trang 13Introduction
Given the importance of investment for a nation’s economic performance, it comes as no surprise that analysis of investment remains a subject of key theoretical and empirical concern to economists Within the broad corpus of work on investment, the real options approach to investment literature focuses on how uncertainty affects investment when investment expenditures are irreversible and when there is uncertainty over the future returns from the investment The theoretical work in this literature shows that greater uncertainty may have a negative or a positive impact on irreversible investment depending on the choice of parameters of the underlying model Although most empirical work finds a negative association between uncertainty and investment, empirical results are far from establishing a consensus about the sign of the uncertainty-investment relationship
In this work, I utilize a theoretical framework from the real options approach to investment literature to study the effects of demand and supply uncertainty on the level of investment I investigate whether or not demand and supply uncertainty have similar effects on investment I also study the extent to which the uncertainty-investment relationship is affected by sunk costs I make three contributions to the existing literature First, I develop and implement a methodology for analyzing the effects of both demand and supply uncertainty on the level of investment Second, I explore the extent to which the uncertainty-investment relationship is affected by the magnitude of sunk costs Third, I address the econometric issues raised by estimating a regression equation in which generated regressors appear
Trang 14optimally derived investment trigger The investment trigger is a function of sunk costs, demand and supply uncertainty, and the price elasticity of demand There is a negative relationship between the trigger and the level of investment
Theoretically, I show that all else equal (a) An increase in sunk costs decreases the level of investment, (b) An increase in demand uncertainty decreases the level of investment, and (c) An increase in supply uncertainty decreases (increases) the level of investment if demand is price elastic (inelastic) I then build on these theoretical predictions and construct an estimation equation for the level of investment According to this methodology, sunk costs interact with the demand and supply uncertainty measures in the estimation equation
I test the theoretical results using two different data sets In Chapters 1 and 2, I evaluate the effects of industry-wide uncertainty and sunk costs on the level of investment in 2-digit Turkish manufacturing industries using panel data over the years 1977-2000 In Chapter 3, I investigate how demand and supply uncertainty, as well as sunk costs affect the aggregate manufacturing investment in thirteen OECD countries using panel data over the years 1975- 2001
I construct measures for both demand and supply uncertainty in my empirical analysis in Chapter 1 Both of these measures are industry-specific I use data on industry output and hourly labor input to construct an index of labor productivity The sample variance of this index is the supply uncertainty measure I use inventory data to construct a series of annual change in inventories The sample variance of this series is the demand uncertainty measure Using industry-specific data, I construct a ratio of fixed assets to net wealth, which is the sunk cost
Trang 15In Chapter 1, I use time-invariant uncertainty measures However, the assumption that uncertainty measures are time-invariant is difficult to credit based on my sample In addition, almost all empirical work uses conditional variance Therefore in Chapter 2, I study the effects of industry-wide uncertainty and sunk costs on the level of Turkish investment using time- variant uncertainty measures I use a univariate ARCH (1) method to model both uncertainty measures, Consequently, I exploit both the cross-sectional and time-series variation in the data The data set is the same as in Chapter 1 In order to make sound inferences from the estimation results, I make the necessary allowances in calculating the standard errors of the estimated coefficients using a methodology introduced by Pagan (1984) I extend his methodology to accommodate multiple generated regressors and interaction variables
My empirical findings in both Chapters 1 and 2 show that the effect of supply uncertainty measure on investment is negative in full sample estimates and in the sample of industries where demand is price elastic On the other hand, the estimated impact of supply uncertainty on investment is positive in the sample of industries where demand is price inelastic In addition, I find that investment is negatively associated both with the sunk cost measure and the demand uncertainty measure Finally, the empirical results show that the interaction variables explain much of the variation in the level of investment across industries in the data This is an important finding because the impact of interaction variables on the level of investment has not been fully studied in the existing empirical literature
Trang 16univariate ARCH (1) method to model both uncertainty measures I use Turkish industry- specific sunk cost proxy and the country-specific industry distribution in the manufacturing sector across the countries in my sample to construct the country-specific sunk cost proxy All data is aggregate and country-specific
The key empirical findings are (1) The negative quantitative impact of the demand uncertainty on investment is larger than the impact of the supply uncertainty measure Estimated at sample means, the investment ratio decreases by 1.7 percent for one percent increase in the demand uncertainty measure, whereas it decreases only by 0.5 percent for one percent increase in the supply uncertainty measure, and (2) The estimated effects of the sunk cost proxy and the demand uncertainty measure are negative and statistically significant Consequently, I find that greater demand uncertainty and higher sunk costs lower the level of investment across countries
The empirical results also show that the methodology developed in this Chapter provides quite a good fit for the data on the OECD countries In fact, the adjusted RỂ is higher when compared to similar empirical studies on the investment-uncertainty relationship across the OECD countries In addition, the estimation equation is a relatively simple model with a parsimonious fit Therefore, the empirical results suggest that interaction variables are key in studying sign of the uncertainty-investment relationship
Trang 17is not a perfect proxy It is only partially based on individual country data Therefore, it is desirable to construct a sunk cost measure based on each individual country’s data on net wealth and fixed assets in future work Another possibility is to construct alternative proxies for sunk costs, which could detect advertising or R&D expenditures
Trang 18Chapter 1: The Impact of Macroeconomic Uncertainty and Sunk Costs on Investment 1.1 Introduction
Although the uncertainty-investment relationship has received a great deal of theoretical attention in recent years, there is little consensus about the sign of this relationship Different theories emphasize different channels, some pointing to a positive and some to a negative relationship The real options approach to investment literature focuses on how uncertainty affects investment when investment expenditures are irreversible This literature shows that when investment is irreversible and can be delayed, it becomes very sensitive to uncertainty over future payoffs These models show that the effect of greater uncertainty on investment depends on the underlying parameters of the model The empirical literature is scant compared to its theoretical counterpart and is mostly confined to a few single-country studies focusing on the UK and the US The empirical results indicate that investment may increase or decrease when there is greater uncertainty
I analyze the impact of macroeconomic uncertainty on the level of investment in 2-digit Turkish manufacturing industries using panel data for the years 1977-2000 In addition, I explore the extent to which the uncertainty-investment relationship is affected by the degree of industry-wide irreversibility, an issue that has not received much attention in existing empirical work,
Trang 19effects of both demand and supply uncertainty on investment across industries Third, I examine the importance of industry-specific sunk costs as a determinant of investment According to the empirical model I develop, sunk costs interact with supply and demand uncertainty In other words, the effect of uncertainty on the level of investment varies with how high sunk costs are The empirical results indicate that these interaction variables are important in understanding investment behavior across industries in Turkey
In the next section, I review the theoretical and empirical results in the real option approach to investment literature Section 1.3 introduces the theoretical model Section 1.4 lays out the methodology Section 1.5 describes the data and explains the construction of the demand and supply uncertainty measures Section 1.6 presents the empirical results and Section 1.7 concludes
1.2 Literature review
Economists have been trying to develop models that would explain and predict changes in investment spending for some time The most frequently used specifications for the analysis of investment spending have been the neoclassical model pioneered by Jorgenson (1963) and the q-theory of investment developed by Tobin (1969)' The basic foundation of these models is the net present value rule According to this rule, investment is undertaken when the present value of expected cash flows is at least as large as the costs The net present value rule, however, is silent on the irreversible nature of investment projects’ Most investment expenditures, however, are at least in part irreversible In other words, sunk costs cannot be recovered should market conditions change adversely
' Chirinko (1993) offers a review of the literature on investment
* One can always redefine the net present value rule by subtracting from the conventional calculation the opportunity cost of exercising the option to invest, and state the rule as “invest if NPV is positive.” Such a terminology is fine as long as the definition includes all relevant option value characteristics of investment
Trang 20The real options approach to investment literature (McDonald and Siegel (1986), Bertola (1988), Pindyck (1988), Bertola and Caballero (1994), and Dixit and Pindyck (1994), Dixit, Pindyck, and Sodal (1999)) shows that the ability to delay an irreversible investment can profoundly affect the decision to invest According to this literature, a firm with an opportunity to invest is holding an option analogous to a financial option When a firm makes an irreversible investment, it kills its option to invest It gives up the possibility of waiting for new information to arrive that might affect the desirability or timing of investment This literature also shows that the opportunity cost is highly sensitive to macroeconomic uncertainty over future project values, so that changing market conditions that affect the riskiness of future cash flows can have a large impact on investment spending These models focus on the threshold that triggers investment and explore whether it depends on measures of uncertainty Investment is undertaken only when projects yield an expected return that hits or exceeds this optimally derived trigger’ Dixit and Pindyck (1994) show that an increase in the investment trigger
decreases the level of investment
Although the real options approach to investment literature has received a great deal of theoretical attention in recent years, there is little consensus on how uncertainty affects irreversible investment One body of this literature (Bertola 1988, Dixit 1989, Bertola and Caballero 1994) shows that greater uncertainty about the expected future returns of investment projects increases the investment trigger Consequently, greater uncertainty leads to less willingness to invest This result occurs because the firms anticipate that the irreversibility constraint may bind in the future and thus are more reluctant to invest today Abel and Eberly
* Empirical work shows that the investment trigger is typically three or four times higher than the Jorgensonian user cost of capital Abel and Eberly (1995) discuss this issue in detail
Trang 21(1995), however, question these theoretical results They show that whether or not uncertainty implies a lower level of investment depends on the parameters of the model
Caballero and Pindyck (1992) and Dixit and Pindyck (1994) show that, when competitive industry equilibrium is considered, there is a negative relationship between industry investment and industry-wide demand uncertainty When imperfect competition is introduced, the intuition behind the above argument remains intact (Dixit and Pindyck (1994)) Even in oligopolistic industries, firms have an incentive to delay investment in the presence of uncertainty Thus, via the irreversibility channel, greater uncertainty is expected to decrease investment However, fear of preemption by a rival and the consequent need to act quickly may counteract the desire to wait Hence, without specifying the nature of a strategic interaction among oligopolists, it appears difficult to predict the sign of the investment-uncertainty relationship under imperfect competition
The theoretical models, therefore, imply that greater uncertainty may have a negative or a positive effect on investment spending depending on the choice of the parameter values of the underlying model and the degree of market power Empirical results mirror these predictions Empirical evidence shows that investment may decrease, increase, or even remain unchanged when there is greater uncertainty
Trang 22demand uncertainty on a panel of Ghanaian manufacturing firms She uses a survey of Ghanaian manufacturing firms, which includes a question on firms’ perceptions of uncertainty in the context of a volatile macroeconomic environment In the survey, firm owners report their probability distribution over future demand for the firm’s products Patillo constructs a variable representing the firm’s uncertainty about future demand conditions based on the survey results Empirical results in this paper support the prediction that firms wait to invest until the marginal revenue product of capital reaches a firm-specific hurdle-level The author also finds that greater demand uncertainty decreases the level of firm investment
At the industry level, Caballero and Pindyck (1996) test whether greater uncertainty increases the investment trigger using panel data on US manufacturing industries The standard deviation of the marginal product of capital is used as a proxy for uncertainty They find that the uncertainty proxy is positively correlated with the trigger and conclude that uncertainty makes investment less likely across US manufacturing industries Using the same data set, Ghosal and Loungani (1996) estimate the impact of price uncertainty on investment Pooling the data for all industries, they find that price uncertainty has no impact on investment However, for industries that have low levels of seller concentration and thus are likely to be highly competitive, the estimated impact is negative and statistically significant For industries with high levels of seller concentration, on the other hand, the estimated impact is always small and not significantly different from zero In a later study, Ghosal and Loungani (2000) study the impact of profit uncertainty on investment using data on US manufacturing Further, they analyze whether or not the sign of the relationship is different in industries that are dominated by small firms versus those that are dominated by relatively larger firms They find that the sign of the investment-
Trang 23profit uncertainty relationship is negative and that the quantitative impact is substantially greater in industries dominated by small firms
Only a few empirical papers analyze investment behavior in Turkey and they all study aggregate investment performance * To my knowledge, there is no empirical study on Turkey that analyzes the effects of industry-wide uncertainty on irreversible investment
1.3 Theoretical framework
I study the impact of uncertainty and sunk costs on industry-wide investment using a model developed by Caballero and Pindyck (1996) Before introducing the theoretical model it is useful to summarize its underlying intuition
The model does not describe the level of investment per se, but rather the critical threshold required to trigger investment However, inferences can be made about the ways in which investment levels should respond to changes in uncertainty An increase in the investment trigger leads to less willingness to invest’ In the model, the average value of a firm’s output fluctuates stochastically Firms invest when the average value of industry output hits an optimally derived investment trigger’ I interpret this decision to invest by firms as entry of new competitors in an industry The investment trigger, which is industry specific, is a function of sunk costs, demand and supply uncertainty, and other parameters
The model is an industry analysis and assumes that firms are risk-neutral, identical and have rational expectations about the underlying stochastic processes and the decision rules of
* Guncavdi, Bleaney and McKay (1996) develop a theoretical model on sectoral private investment in Turkey
° An increase in uncertainty or sunk costs should reduce investment because project values that were above or close to what was a lower threshold are now below a higher threshold Dixit and Pindyck (1994) and Pindyck and Solimano (1993) discuss this issue in length
Trang 24the other firms’ The variables in the model are all implicitly indexed by the industry subscript i Total industry output, Q(t), is given by
Q(t) = N(H)A(t) (1)
where N(t) is the number of firms in an industry and A(t) follows an exogenous stochastic process representing aggregate supply shocks Industry demand is given by
P(t) = D()9(4) `" 2)
where P(t) is the price level, D(t) follows an exogenous stochastic process representing aggregate demand shocks, and 1 is the price elasticity of demand
To introduce irreversibility, I assume that entry by new firms in an industry requires a sunk cost K The free entry condition (Leahy, 1993) implies that firms will enter until expected profits are driven to zero:
K> Bị j0AQua (3)
0
where 6 is the discount rate, and y is an exogenous rate at which firms exit the industry and will follow dIN(t)/dt= -yN Using Eq (1) and (2), the average value of output (B(t)) in an industry is given by the following
B(t) = P(t)A(t)
B()= D()A(*"N(}" (4)
Assume A(t) and D(t) follow uncorrelated geometric Brownian motion Thus, the dynamics of A(t) and D(t) are:
’ It would be more reasonable to assume that firms are heterogeneous and that they have different thresholds Then the decision to invest will depend not only on industry output but also on the number of firms currently producing This would add at least one more state variable to the problem and instead of industry-specific threshold I would have to solve for individual firm’s thresholds Since this is an industry-level analysis, following Caballero and Pindyck, I decided not to pursue that venue
Trang 25diog D(t)=(a, -1/26,° jit + ,4z,(t) (5)
dlog A(t)=(c, -1/26,’ hit +o, dz, (t) (6)
where Og, &, and ont ; o are constant drift and volatility parameters, respectivelyỶ dz, and dz, are uncorrelated Weiner processes Then B(t) follows a simple regulated geometric Brownian motion with volatility: 2 ¬] om [act ) G TỊ and with a rate of drift: _¥ -loz\facly, le c= La(a, 254 }*| n | s91) (8) Observe from (8) that the rate of drift is negatively associated with the variance of (7)
aggregate demand shocks (ơi) The effect of supply shocks (ø; ), however, depends on the magnitude of the price elasticity of demand If demand is price elastic, n>1, (inelastic, n<1) then the rate of drift is negatively (positively) associated with G; Therefore, given the magnitude of the price elasticity of demand, G? may have different effects on the rate of drift Later in this section, I show that the sign of the relationship between the investment trigger and uncertainty may be positive or negative depending on the type of uncertainty and the magnitude of the price elasticity of demand
* This model assumes that volatility is constant If volatility can change then, in principle, the process by which it changes should be part of the model However, models of financial option valuation in which volatility follows a stochastic process suggest that assign this complication would not change the basic results See Scott (1987) and Wiggins (1987)
Trang 26This model is simple enough to find a closed form solution for the optimal investment rule using dynamic programming’:
K| (6+ y)OtNS 226+ 100 6)
2
where U is the investment trigger’ The optimal investment rule for firms in an industry is
_ số ØU
when B(t) reaches the investment trigger U It is quite straightforward from (9) that 2K >0, all else equal higher K is associated with a higher U
Re-write Eq (9) as
2
Given (10), I examine the effects of demand and supply uncertainty on the investment trigger First, I show how changes in demand uncertainty affect the trigger: đU _ôU đc, >0 (11) do, 0 do, A higher value of 6, raises U by increasing the opportunity cost of investing, and thereby x dU raising the investment trigger Next, I show that the sign of dc depends on the magnitude of do a ` or U price elasticity of demand Evaluating the derivative at n=1 yields v = ( Suppose there is a Oo a
* Caballero and Pindyck (1996) also derive (9)
'° All the firms face the same boundary U, which implies that the investment trigger U is the same for any firm in the industry
Trang 27positive supply shock, which causes sales to increase and prices to decrease When demand is unit elastic, any sales effect is offset by the reduction in prices Consequently, supply-side shocks do not affect the volatility [(7)] or the rate of drift [(8)] of average industry output and
dU
therefore do not affect investment decisions When #1, the sign of dc depends on the magnitude of the price elasticity of demand, i.e n>1 or n<1
Case 1: Demand is price elastic (n> 1):
dU _ aU a
——= >0 (12)
do, 0 do,
Case 2: Demand is price inelastic (n<1):
đU _êU dc do, do, <0 (13)
The effect of supply uncertainty on the investment trigger depends on the magnitude of price elasticity of demand If demand is price elastic, n>1, (inelastic, n<1) the relationship is positive (negative) When demand is price inelastic the sales effect overcomes the reduction in prices, which leads to an increase in revenues and causes investment to increase
A novelty of my theoretical analysis is that I evaluate the effects of both supply and demand uncertainty on the investment trigger By doing so, I show that the sign of the uncertainty-investment relationship not only depends on the magnitude of price elasticity of demand but also on the type of uncertainty Based on the analysis of (9), I summarize the results
as follows: All else equal, (a) Higher sunk costs are associated with a higher investment trigger, (b) An increase in demand uncertainty increases the investment trigger, (c) An increase in
Trang 28supply uncertainty increases (decreases) the investment trigger if demand is price elastic (inelastic)
1.4 Methodology
The theoretical model states that the optimal investment rule in an industry is when the average value of output in an industry reaches the investment trigger For the empirical implementation, I interpret this condition more broadly to imply that given the industry-specific investment tirgger
Investment>0 if B,,>U, 1
Investment=0 otherwise
The level of investment in industry i at time ¢ is assumed to be a linear function of [B, -U, |" The level of investment is inversely related to the investment trigger, ie an increase in investment trigger implies lower level of investment I use data on industry value- added as a proxy for industry characteristics Assume the level of investment in industry i at
time f as
L, = (B, ~~ Ui, ) VA +i, (14)
where VA, , is value added in industry i at time ¢ and 0, , is the error term’” Re-write (14) as VA ( : =(B,,-U,,)+0,, (15) i,t To develop an estimation equation, I expand (9) using a Taylor series approximation’* This process yields: "' For analytical tractability, I assume a linear relationship It is desirable to investigate this assumption in future work
T implicitly assume that value added is representative of the overall industry conditions and that the average level of investment in a given industry depends on how healthy that industry is
Trang 29UnxK(, +602 +6,03 +£,0702 +£,0/ +804) (16) Using (15) and (16) I estimate the following equation using an OLS panel regression 4 4 an =p +B, K,, +B)B,, +B,0;;K,, +B494,K,, + B50, ;04;K,, +P5O,;K;, +B Oa;K;, +8, (17) where t dể the ratio of investment to value added, K;, 1s the sunk cosfs, B,,is industry 2 : : 2 :
output, o ;1s the supply uncertainty measure, 64; is the demand uncertainty measure €;, is an iid error term Subscripts i and ¢ index the industry and time period
1.5 Data and preliminary statistics
The data set comprises sixteen 2-digit Turkish manufacturing industries over the years 1977 to 2000 To the best of our knowledge, the data on private gross fixed investment has not been used in any previous empirical investment study Appendix A describes all variable definitions in detail, including data construction and sources
Figures 1 and 2 show real growth rates for gross domestic product (GDP) and annual inflation rates in Turkey, respectively Although Turkey has averaged 4.0% growth since 1980, economic growth has been quite volatile as seen in Figure 1 Oil-price shocks in the 1970s and related balance of payments problems contributed an economic recession in the late 1970s After the introduction of a stabilization and liberalization program in 1980, the government was able to accelerate economic growth in the following years However, after 1986, the volatility of annual GDP growth rates increased Other events such as the 1900-1991 Persian Gulf crises, the 1994 Turkish financial crises, 1998 Russian crises and 1999 earthquakes contributed to output volatility in the economy
Trang 30
Volatile output growth has been accompanied by chronic inflation in Turkey As seen in Figure 2, Turkey experienced accelerations in whole price index (WPI) inflation in 1977-1978, 1979-1980, 1987-1988, and 1994 Major events associated with these increases in inflation rates are oil price shocks in 1978-1979, balance of payments crisis in 1977-1980, devaluation of 1980 and 1994 financial crisis
Figure 3 plots investment in manufacturing in 1985 US Dollars Investment, too, has been volatile in Turkey Events such as the 1990-1991 Persian Gulf crises, the 1994 Turkish financial crises, and 1999 earthquakes contributed to investment volatility in Turkey An IMF report, which analyzes the private investment behavior in Turkey, concludes, “Overall investment behavior in Turkey followed a modest upward trend but was never able to reach to the economy’s potential (due to macroeconomic instability and uncertainty)”
Table 1 presents the investment-output, investment-value added ratios, and industry investment as a share of aggregate investment in manufacturing The first two columns of the Table show that there is variation in the average values of both ratios across industries Wood products, professional, scientific and controlling equipment, and transport equipment industries are among the industries that have the highest average investment ratios The machinery industry, on the other hand, has the lowest average across industries Although not reported, the time-series behavior of the data on investment ratios does not show any obvious trends However, the time-series behavior of investment varies across industries'* The last column of Table 1 shows industry investment as a share of aggregate investment in Turkish
'* Cited from IMF Report #3452 on Turkey (1997), page 6
'S Only similar industries’ investment series move like each other over the years For example, the correlation between textiles and leather industries is 0.765 (highest correlation in the sample) The lowest correlation in the sample is 0.036 and is between machinery and textiles
Trang 31manufacturing Textiles, machinery, and iron and steel industries have relatively high shares compared to other industries
a Measuring uncertainty
The theoretical framework evaluates the effects of both demand and supply uncertainty on investment The empirical analysis, too, uses measures for both demand and supply uncertainty Both of these measures are industry-specific I use data on industry output and hourly labor input to construct an index of labor productivity'® The sample variance of this index is the supply uncertainty measure Economic analyses over the years have shown that labor productivity gains are one of the most important sources of economic growth Consequently, volatility in labor productivity directly affects the volatility of output Therefore volatility in an index of labor productivity detects the uncertainty that face firms from the production side G; denotes the supply uncertainty measure
I use inventory data to construct a series of annual change in inventories The sample variance of this series is the demand uncertainty measure Volatility in labor productivity captures uncertainty faced by firms from their production side Among others Metzler (1941), Blinder and Maccini (1991), and Flood and Lowe (1993) have empirically shown that most of the volatility in inventories is representative of volatility coming from the demand side of the economy ơa denotes the demand uncertainty measure
Table 2 presents the uncertainty measures and shows that there is significant variation in both measures of uncertainty across industries The demand uncertainty measure ranges from a high of 14.78 in one industry to a low of 0.52 in another The supply uncertainty measure, on
'® T also used data on value added to construct an alternative index of labor productivity This index is quantitatively very similar to the index of labor productivity used in the empirical analysis Therefore, I use output data to construct an index of labor productivity instead of value added data
Trang 32the other hand, ranges from a high of 31.90 to a low of 6.05 across industries Table 3 too presents some descriptive statistics on the uncertainty measures This table provides further evidence that there is considerable variation in these measures across industries Furthermore, the uncertainty measures are not highly correlated
b Sunk costs
According to the theoretical model, the size of sunk costs is an important determinant of investment’’ Although the size of the industry’s sunk costs is a very useful variable in theory it is very hard to estimate in practice I was able to obtain data on industry fixed assets and net wealth Using this data, I construct a ratio of fixed assets to net wealth This variable detects most, if not all, of the physical assets that firms have incur in order to get established in an industry’* This variable is the sunk costs measure and is denoted by K The sunk costs data is industry-specific
The last column of Table 2 shows the sunk costs measure across industries Some industries require quite substantial amounts of sunk costs relative to others For example, the textiles, iron and steel, and motor vehicle industries are the industries with the highest values of sunk costs The leather, non-metallic and scientific equipment, and pottery industries, on the other hand, have the low sunk cost values
1.6 Empirical results
As summarized in the literature review, almost all empirical work that studies the impact of uncertainty on investment assumes a linear relationship between these two variables
17 One of the predictions of the theoretical model is that, given uncertainty, greater sunk costs will lower investment due to an option value of waiting The model also assumes that the sunk costs cannot be recovered if the firms decide to exit the industry The assumption of complete irreversibility is a simplifying assumption Some recent papers began to model the effects of partial irreversibility
'8 Naturally, this sunk costs proxy does not include all aspects of sunk costs incurred by firms in an industry, such as advertising costs Given that the data is on manufacturing industries, this proxy provides a sensible idea of the magnitude of the physical assets that firms use while entering in a given industry
Trang 33However, these empirical studies do not consider the effects of the interaction variables on investment Therefore, I estimate the following alternate specification:
uit =B,+B,K+B,0; +B,0; +8 (18)
where the independent variables are sunk costs, the demand and supply uncertainty measures, and the level of output
I present the OLS estimation results for (18) in the first column of Table 4 The results indicate that both demand and supply uncertainty are negatively associated with the investment ratio The estimates are statistically insignificant and the magnitude of the supply uncertainty effect is substantially larger Although the estimated impact of sunk costs on the investment ratio is negative, the estimated coefficient is small in magnitude
Before presenting the OLS estimation results for (17), I present the estimation results for the model in which uncertainty measures by themselves are added to (17) in the second column The third column shows the estimation results for (17) The estimation results show that the level of investment is positively associated with the level of output The estimated coefficient for the sunk cost measure is, though small in magnitude, negative as predicted by the theoretical model With the exception of ơøK , all interaction variables have negative estimated coefficients I then compute the F test where under the null hypothesis the coefficients for co and om are zero in specification 2 The F-test and the corresponding p-value at 5 percent suggest that null hypothesis is not rejected and that the coefficients for o? and a4 are zero'”, In addition, a comparison of adjusted R’ values suggests that specification 3 provides a better fit for the investment data in Turkey than specification 1
Trang 34
To check the robustness of my results, I drop two higher order terms [ơ¿K and ơø2K] from specification 1 and present the estimation results in the third column of Table 4°° The results show that the investment ratio is positively associated with the interaction of sunk costs with supply uncertainty but negatively associated with the interaction of sunk costs with demand uncertainty In this specification the estimated coefficient for sunk costs is negative and Statistically significant
In order to capture the marginal effects of sunk costs and the demand and supply
uncertainty measures, I calculate the net effects of change in each variable First, I evaluate the estimated coefficients from specification 3 in Table 4 at sample means Then, I evaluate the estimated coefficients from specification 3 in Table 4 at the iron and steel industry and at the pottery and earthenware industry sample values, so that I can better understand the net effect of change in sunk costs and the uncertainty measures for a range of sample values The iron and steel industry (pottery and earthenware industry) is representative of relatively high (low) sample values of sunk costs and the demand and supply uncertainty measures I report the coefficients for the marginal effects in Table 5”!
Trang 35Next, Table 5 presents the net effect of changes in the demand uncertainty and supply uncertainty measures, respectively The results show a negative relationship between the investment ratio and the demand uncertainty measure The investment ratio decreases by 8.7 percent for a one percent increase in the demand uncertainty measure, when coefficients are evaluated at sample values of the iron and steel industry These results confirm the theoretical findings and show that the negative impact of demand uncertainty on the investment ratio is quantitatively stronger when there is greater uncertainty and higher sunk costs In addition, all of the coefficients are significant
The net effect of change in supply uncertainty, on the other hand, can be negative or positive depending on the magnitude of other variables Table 5 shows that when all variables are evaluated at their sample means, a one percent increase in the supply uncertainty measures causes an 8.7 percent decrease in the investment ratio This negative effect becomes positive, however, when the coefficients are evaluated at the sample values of pottery, chine, ceramic, and earthenware industry In fact, the investment ratio increases by 3.1 percent for one percent increase in the supply uncertainty measure These results suggest that when there is relatively low sunk costs and uncertainty, supply uncertainty does not necessarily decrease investment They provide further proof that the interaction variables are important in understanding the uncertainty-investment relationship
Since this is a pooled estimation, there is the possibility of parameter heterogeneity If the parameters differ across industries, then the pooled estimation imposes an invalid inequality restriction Such a restriction will lead to inconsistent estimates Although the ideal way to approach this problem is to estimate each industry separately, I was not able to do this due to
Trang 36data limitations Therefore, this possibility is investigated by separating industries based on the magnitudes of price elasticity of demand
Unfortunately, there is little research on the magnitudes of price elasticities across industries in Turkey Consequently, these results should be viewed merely as suggestive The grouping of industries is based on preliminary research undertaken by the State Institute of Statistics of Turkey The study uses data available from the Consumer Expenditures Survey and surveys of published economic and marketing research Based on these studies, eleven of the industries are grouped as elastic and the remaining five as inelastic” I present the estimation results based on sub-samples in Table 6 The estimated coefficients for all interaction terms are negative in both groups However, the estimated coefficients are significant only in the elastic industry group The estimated impact of sunk costs on the investment ratio is quantitatively small in the inelastic industry group Industry output is positively associated with the investment ratio in both groups
I calculate the net effect of changes in the sunk cost measure and the demand and supply uncertainty measures using the estimated coefficients from specification 3 in Table 6 Table 7 reports the results The adjusted coefficients show that the net effect of a change in sunk costs on the investment ratio is negative for a range of sample values in both groups The net effect of a change in demand uncertainty is also negative in both groups As the theoretical model predicts, the net effect of change in supply uncertainty is negative in elastic industries In inelastic industries, the net effect of supply uncertainty is only positive when all variables are evaluated at their relatively low sample values This result may indicate that not only the price
* The following industries have inelastic demand: Man of food and beverage, Man of industrial chemicals, Man of iron and steel, Man of non-ferrous metal basic industries, and Man of transport equipment The remaining industries are assumed to have elastic demand
Trang 37elasticity of demand but also relative values of demand uncertainty and sunk costs affect the investment supply uncertainty relationship
Overall, these empirical results confirm the theoretical predictions The results indicate that interaction of sunk costs with the uncertainty measures explain the differences in investment behavior more successfully than the uncertainty measures by themselves
1.7 Conclusion
The real options approach to investment literature emphasizes three important features of investment projects that have been overlooked by previous models: the irreversibility of investment projects, uncertainty about expected future returns, and the timing of investment projects I extend the theoretical findings in this literature by showing that the effect of supply uncertainty on investment depends on the magnitude of the price elasticity of demand If demand is price elastic (inelastic) the relationship is negative (positive) I then empirically explore the relationship between investment, uncertainty and sunk costs using panel data on 2- digit Turkish manufacturing industries I find that the effect of uncertainty on the level of investment varies with how high sunk costs are
The key empirical findings are: (1) Investment is substantially more sensitive to the demand uncertainty measure than to the supply uncertainty measure, (2) The effect of supply uncertainty on investment is negative in full and elastic sample estimates The estimated impact of supply uncertainty on investment is only positive in inelastic industries when both demand uncertainty and sunk costs are relatively low The estimated effect of demand uncertainty on investment, on the other hand, is always negative, and (3) Investment is negatively associated with the sunk costs measure In addition, the empirical results show that the interaction variables explain much of the variation in investment behavior across industries This is an
Trang 38important finding because the impact of interaction variables on investment behavior has not received much attention in the empirical literature These results shed some light to different empirical findings on the sign of the investment-uncertainty relationship They underline the importance of identifying what type of uncertainty the uncertainty measures used in the empirical analysis represent My empirical results are robust to grouping the sample of industries based on the price elasticity of demand
While my results are encouraging, further research is desirable in certain areas First, the measures of demand and supply uncertainty used in this paper are measured as sample variances and are time-invariant The assumption that uncertainty measures are time-invariant is extremely difficult to credit I will explore this issue in future work Second, it would be desirable to obtain more accurate data on industry demand elasticities Finally, it is possible that the demand and supply uncertainty measures are endogenous Endogeneity needs to be taken into consideration in interpreting the empirical evidence I was not able to find good instruments for the uncertainty measures due to data limitations at the industry level Still I re-estimated (17) using the sample variance of wage rate (as an instrument for supply uncertainty) and the sample variance of the consumer price index (as an instrument for demand uncertainty) The results are not reported here but they are not very different from the previously reported results However, the precision is poor among the coefficients
Trang 39Figurel: Real GDP Growth Rates (percent) in Turkey, (1970-2000) Source: State Institute of Statistics, Turkey 10 +1 percent _ L 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Years