The Long and the Short of It: Uncertainty,Irreversibility and Heterogeneous Investment

Dynamics in Italian Company Data

Stephen R. Bond¤and Domenico Lombardiy

March 2001

Abstract

We use Italian company data to test for the presence of real optionse¤ects induced by uncertainty and irreversibility on …xed capital invest-ment. Our approach recognises that …rm-level investment spending maybe aggregated over multiple investment decisions. Following Bloom, Bondand Van Reenen (2001), our empirical speci…cation emphasises e¤ects ofuncertainty on short run investment dynamics. We …nd evidence that un-certainty and (partial) irreversibility produce heterogeneous and non-lineardynamic responses of investment to demand shocks. We control for cash‡ow e¤ects and explore the robustness of our results to di¤erent measures ofuncertainty available from the Bank of Italy Survey of Investment in Man-ufacturing. Results are obtained using GMM estimation procedures as inArellano and Bond (1991 and 1998), using an unbalanced sample coveringabout 800 …rms per year between 1984 and 1998.

Keywords: Investment, uncertainty, irreversibility, real options, paneldata.

JEL Classi…cation: D92, E22, D8, C23.Acknowledgments: The authors thank Nick Bloom for helpful commentsand the ESRC Centre for Fiscal Policy at IFS, Oxford University, the ESRCand the FCO for …nancial support. The opinions expressed are those of theauthors and do not necessarily re‡ect the views of the Banca d’Italia or theEurosystem.Correspondence:domenico.lombardi@nuf.ox.ac.uk; Nu¢eld College, Ox-ford, OX1 1NF, UK.

¤Institute for Fiscal Studies and Nu¢eld College, Oxford.yNu¢eld College, Oxford and Research Department, Banca d’Italia.

1. Introduction

The relationship between uncertainty and investment has long been of interest to

both economists and policymakers. Important early contributions include those

of Lucas and Prescott (1971), Hartman (1972), Nickell (1977a and 1977b) and

Abel (1983). In the last decade research has focused on a class of models in which

real options in‡uence …rms’ investment behavior (Bertola (1988), Pindyck (1988),

Caballero (1991), Dixit and Pindyck (1994); see also the recent survey by Carruth,

Dickerson and Henley (2000)).

Theoretical analyses have shown that the impact of uncertainty on the level of

the capital stock in the long run is ambiguous (Abel and Eberly (1995); Caballero

(1999)). Perhaps for this reason empirical studies have not reached any consensus

on the sign or signi…cance of this long run relationship. More recent theoretical

contributions have emphasized the e¤ects of uncertainty on short run investment

dynamics (Abel and Eberly (1996); Bloom (2000)) and empirical studies have

found evidence consistent with the predicted slower response of investment to

demand shocks at higher levels of uncertainty (Guiso and Parigi (1999); Bloom,

Bond and Van Reenen (2001)).

This paper extends this empirical research using data on Italian …rms. In

particular we test for the non-linear and heterogeneous investment dynamics pre-

dicted by models with partial irreversibility and uncertainty. We construct mea-

sures of uncertainty based on the errors made in forecasting future investment

or employment growth by …rms in their responses to the Bank of Italy’s Survey

of Investment in Manufacturing (SIM). We …nd that investment responds more

1

slowly to real sales growth for …rms which face a higher level of uncertainty.

The paper is organized as follows. In Section 2 we provide a brief review of

the most recent literature; in Section 3 we present our econometric speci…cation,

while in Section 4 we describe the features of our dataset and our measures of

uncertainty; in Section 5 we present the empirical results, and Section 6 concludes.

2. A Review Of The Recent Literature

The e¤ect of uncertainty on business investment decisions has long been debated in

economics. According to early contributions by Hartman (1972) and Abel (1983),

an increase in uncertainty leads to more capital accumulation; however, the posi-

tive relation between uncertainty and investment, derived in a static framework in

their model, holds only in the presence of rather restrictive assumptions regarding

the nature of the …rm’s revenue function and the demand shocks it faces. Fur-

thermore, the labor input in the production function is assumed to be perfectly

‡exible, an assumption that has been called into question by empirical research

on labor demand (see, for example, Nickell (1986)) and relaxed on later.

Caballero (1991) further investigates this relationship, showing that it re‡ects

the interaction between the degree of capital irreversibility and market competi-

tion. In the case of imperfect competition, an increase in uncertainty depresses

long-run investment the greater the capital irreversibilities, i.e. the more asym-

metric the shape of the adjustment cost function.

If capital is irreversible, …rms have an incentive to wait until more information

becomes available. On the contrary, when a …rm does invest “...It gives up the

possibility of waiting for new information to arrive that might a¤ect the desir-

2

ability or timing of the expenditure; it cannot disinvest should market conditions

change adversely...” (Dixit and Pyndick (1994) p. 6). This generates a real option

for a …rm insofar as it can choose the most appropriate time for implementing its

investment decision. The resulting ‘caution e¤ect’ is modelled by widening the

upper threshold for investment: investment occurs only when the gap between

the current capital stock and the optimal one becomes “large enough”.

The theory underlying real options has shed important light on aspects of

…rms’ investment decisions. However, up until now its predictions have not been

widely tested. One reason for this is that the theory relates to a single investment

decision, while in reality we tend to observe more aggregated data.

Bertola and Caballero (1994) characterize the aggregate implications induced

by irreversible investment under uncertainty on a population of homogenous …rms

operating with a single line of capital. They generate an intermittent investment

process at …rm level and show how non-linear microeconomic investment rules

may lead to a smooth time series for aggregate investment. They are the …rst - to

our knowledge - to analyse short run investment dynamics when the investment

decision is characterized by both uncertainty and irreversibility.

Bloom (2000) investigates the real option e¤ect of uncertainty on investment

dynamics and shows that it does not a¤ect the long run rate of investment. This

result follows from the fact that the real option e¤ect of uncertainty and irre-

versibility increases investment thresholds. Although this reduces investment dur-

ing times of buoyant demand, it also reduces disinvestment when demand is low:

in the long run these two e¤ects cancel each other out. As the optimal investment

3

policy under conditions of uncertainty and irreversibility in investment projects is

characterized by a wider threshold rule, those two factors unambiguously reduce

the short term response of investment to an exogenous shock. This logic helps

to explain why Caballero (1991) and Pindyck (1993) both report a negative ef-

fect of uncertainty on investment in their two-period models. Bloom (2000) also

shows that uncertainty and irreversibilities induce richer short term investment

dynamics by means of …rms’ lagged responses to past demand shocks. Di¤erently

from Bertola and Caballero (1994), Bloom models a population of heterogenous

…rms operating with multiple lines of partially irreversible capital and labor, and

makes less restrictive assumptions regarding the nature of the demand process

and the production function facing …rms. In addition, his results are robust to

any level of aggregation from the single line of capital up to the industry and the

whole macroeconomy. This result takes into account the feature that, although

…rms may undertake infrequent and large adjustments in their stocks of particular

capital goods when following a threshold-based investment policy, this lumpiness

becomes less evident when observing aggregated investment data, even at the …rm

level.

Bloom, Bond and Van Reenen (2001) develop a theoretical framework for an-

alyzing the dynamics of irreversible investment under uncertainty and test their

resulting predictions using data on a sample of listed UK companies. They predict

an increasing marginal response of investment to shocks and measure uncertainty

as the idiosyncratic volatility in the …rms share prices. Their results suggest that

higher levels of uncertainty weaken a …rm’s responsiveness to demand shocks by

4

postponing its decision to invest due to the “caution e¤ect”. In addition, uncer-

tainty appears to drive investment dynamics mainly in the short run; the long

run e¤ects of uncertainty on capital accumulation are ambiguous. Their approach

is applicable across di¤erent levels of aggregation and can accommodate a wide

range of demand processes and production technologies. However, their measure

of idiosyncratic uncertainty may be criticized on the grounds that share prices

are also a¤ected by noise traders, speculative bubbles and irrational exuberance,

and these sources of volatility may not be relevant to a …rm when making invest-

ment decisions. Finally, one might object to the fact that their sample, being

biased towards the large and multinational …rms listed on the London stockmar-

ket, makes it di¢cult to apply their …ndings to the entire population of …rms in

the manufacturing sector.

Guiso and Parigi (1999) investigate the relation between irreversible invest-

ment and uncertainty using data from a cross-sectional survey representative of

the Italian manufacturing sector that reports managers’ assessments of the distrib-

ution of future demand for their products. They de…ne a measure of idiosyncratic

uncertainty closely related to …rms investment decisions and test for a number

of theoretical predictions by comparing the amount of investment undertaken by

…rms with di¤erent degrees of market power and factor substitutability, access

to credit markets and liquidity constraints. Their evidence strongly suggests a

negative relation between uncertainty and investment.

5

3. Econometric Speci…cations Of Investment Behavior

3.1. A Baseline Dynamic Model

In few …elds has the economics literature proposed so many di¤erent structural

models of behaviour supported by such fragile empirical evidence as is the case

in the …eld of business investment. As a result of that, researchers have often

resorted to estimating reduced form empirical investment equations that exhibit

reasonable short and long run properties, even though they are not explicitly

derived from models of optimal capital stock adjustment. The Error Correction

Model, which allows for a ‡exible adjustment of the capital stock towards its long

run equilibrium value, is a commonly used speci…cation in this context. This

makes it particularly suitable for testing the null hypothesis of no real option

e¤ects on investment.

Consider the simplest world with no uncertainty or investment irreversibility

and de…ne kit as the optimal, frictionless level of the capital for the …rm i in period

t. Then by de…ning, respectively, yit and jit as the real output and the user cost

of perfectly reversible capital, we can write the optimal capital stock as a function

of a quantity variable and a set of price variables embodied in the cost of capital:

kit = a + yit ¡ Ájit (3.1)

where Á is the input elasticity of substitution. It can be shown that this expression

for the level of capital represents the solution to the static maximization problem

of a …rm operating with a constant returns to scale CES technology, and, for

values of Á equal to unity, it encompasses the case of a Cobb-Douglas production

6

function (see the survey by Bond and Van Reenen (1999)).

However, …rms have to bear some costs during the transition to a new equi-

librium level of capital, typically due to the installation of new equipment, the

resulting dislocation of ongoing productive activities, the need for training the

workforce to use the new equipment and the burden imposed upon the manage-

rial and administrative skills of existing planning sta¤. In the presence of such

adjustment costs, the actual capital stock does not adjust immediately to changes

in the optimal frictionless level. An econometric speci…cation that captures dy-

namic adjustment of the capital stock is an autoregressive-distributed lag (ADL)

model such as:

kit = ®0+®1ki;t¡1+®2ki;t¡2+¯0yit+¯1yi;t¡1+¯2yi;t¡2+°0jit+°1ji;t¡1+°2ji;t¡2+²it

(3.2)

Bean (1981) …rst introduced a now widely tested speci…cation of the investment

equation that separates out short and long run dynamics. Assuming that variation

in the cost of capital can be accounted for by additive year-speci…c e¤ects (¹t)

and …rm-speci…c e¤ects (´i), then a convenient reparameterisation of (3.2) gives

the error correction model:1

¢kit = ¹t + (®1 ¡ 1)¢ki;t¡1 + ¯0¢yit + (¯0 + ¯1)¢yi;t¡1 (3.3)

¡(1¡ ®1 ¡®2)(k ¡ y)i;t¡2

+[¯0 + ¯1 + ¯2 ¡ (1¡ ®1 ¡ ®2)]yi;t¡2

+´i + "it:

1Cf. Bond and al. (1997), Bond, Harho¤ and Van Reenen (1999) and Mairesse (1999) forrecent applications to micro data.

7

The above model allows for rich short run dynamics and incorporates a feed-

back mechanism on the same lines as that proposed by Davidson, Hendry, Srba

and Yeo (1978) and Hendry (1980).

To allow for an interaction between the real and …nancial decisions of …rms,

the ratio of cash ‡ow to the beginning-of-period capital stock can be introduced

into the econometric model. Although such an e¤ect is not the main focus of

our paper, previous studies have found that cash ‡ow may in‡uence the …rm’s

investment rate. Consequently, we control for the cash ‡ow e¤ect to avoid the

possibility that any uncertainty e¤ect that we identify may simply be proxying for

omitted liquidity e¤ects. These considerations, together with the approximation

that ¢kit ¼ Iit=Ki;t¡1 ¡ ±i, where ±i is a …rm-speci…c depreciation rate, suggest

the following model of the investment rate:ÃIitKi;t¡1

!= ¹t + ½1

ÃIi;t¡1Ki;t¡2

!+ !0¢yit + !1¢yi;t¡1 (3.4)

+µ(k ¡ y)i;t¡2 + ³yi;t¡2 + Ã0ÃCitKi;t¡1

!

+´i + "it:

It should be noted that the unobserved …rm-speci…c e¤ects (´i) also allow for

variation across …rms in the price elasticity of product demand. A negative value of

the parameter µ is required for the estimated dynamics to be consistent with error

correcting behavior. Finally, in the context of a CES production function with Á 6=

1 the coe¢cient on the lagged level of output tests the long run constant returns

to scale restriction (¯0 + ¯1 + ¯2)=(1 ¡ ®1 ¡ ®2) = 1 (see (3.3)), although if the

production function is Cobb-Douglas then there should be a long run proportional

relationship between capital and output regardless of the returns to scale.

8

3.2. A More Structural Approach

Eberly and van Mieghem (1997) derive the threshold rule for investment under

uncertainty with partially irreversible capital and show that it can be represented

by the standard formula for Jorgensen’s cost of capital, and a multiplicative term

proxying for the real option e¤ect. In particular, by de…ning P as a demand term,

K as the …rm’s capital stock and letting 0 < a < 1, they derive the following

threshold-based investment policy:

Table 1: The Threshold-Based Investment PolicyInvestment Region: P 1¡aKa¡1 ¸ b £ ÁIInaction Region: s=ÁD < P

1¡aKa¡1 < b £ ÁIDisinvestment Region: P 1¡aKa¡1 · s=ÁD

Here b and s are the Jorgensonian user costs relevant for buying and selling

capital respectively, with b > s re‡ecting the assumption that capital can only be

sold for a price less than that at which it must be purchased. ÁI and ÁD - both

greater than unity - refer to investment and disinvestment real option e¤ects.

The representative …rm only invests when the marginal productivity of capital

(P 1¡®Ka¡1) hits the upper threshold (b £ ÁI), while capital scrapping only oc-

curs when the lower bound is reached (s=ÁD). It follows that when the marginal

revenue product of capital lies between these thresholds, the …rm does not un-

dertake any investment or disinvestment. However, these periods of inaction are

followed by episodes of occasional investment/disinvestment spikes as documented

by Doms and Dunne (1994, 1999) and Caballero, Engel and Haltiwanger (1995)

9

for the US, Attanasio and Pacelli (2000) for the UK, Anti Nilsen and Schiantarelli

(1998) for Norway, Bigsten et al. (1999) for Africa and Gelos and Isgut (1999) for

Latin America.

Bloom, Bond and Van Reenen (2001) characterize short run responses of …rm-

level total investment to demand shocks (¢pt) and changes in the level of uncer-

tainty (¾t). They show that the derivatives of the investment rate with respect

to demand shocks and uncertainty have the following signs for …rms undertaking

positive investment and disinvestment respectively:

Table 2. Short Run Investment Response@(

ItKt)

@¢pt

@2(ItKt)

@¢p2t

@2(ItKt)

@¢pt@¾t

Investment + + ¡Disinvestment + ¡ ¡

Thus, a positive demand shock unambiguously increases the investment rate

and an increase in the level of uncertainty unambiguously reduces the response

of investment to a given demand shock. For samples dominated by …rms under-

taking positive gross investment, there should be a non-linear and strictly convex

response of the investment rate to demand shocks. In the long run, the sign of

the capital-uncertainty relationship is ambiguous in models of investment with

partial irreversibilities (Abel and Eberly (1995)). To the extent that the level of

uncertainty is constant over time for individual …rms, our empirical speci…cations

allows for any long run e¤ect of uncertainty through the unobserved …rm-speci…c

e¤ects.

10

In the case of investment under complete reversibility, the optimal capital stock

of …rm i in period t can be characterized by the following log-linear speci…cation:

logK¤it = logYit + °

ÃCitKi;t¡1

!+ » i + Àt (3.5)

where the stochastic terms »i and Àt allow, respectively, for …rm-speci…c and time-

speci…c e¤ects.

Given partial irreversibility, the actual capital stock (Kit) and this hypothetical

optimum will di¤er, and need not be equal on average in the long run. However

we can exploit a previous result found by Bloom (2000) according to which the

long run growth rate of a …rm’s capital stock under partial irreversibility and its

hypothetical value under costless reversibility will be equal. Therefore:

logKit = logK¤it +'it (3.6)

with logKit and logK¤it cointegrated, so that 'it is a stationary error term. This

relation does not impose the restriction that 'it should be a mean-zero process

since the average levels of these two concepts of the capital stock may deviate un-

der particular assumptions regarding the reversibility of investments. Combining

(3.5) and (3.6) and using an error correction formulation to capture the stationary

dynamics implied by 'it then leads to an emprical speci…cation similar to (3.4).

To test for the non-linear response of investment rates to demand shocks predicted

by the partial irreversibility model, and the predicted e¤ect of uncertainty on the

response of investment rates to demand shocks, we include additional terms in

the square of current output growth (¢y2it) and the interaction between current

output growth and a measure of uncertainty (¾it¢yit). Including these additonal

11

terms gives our estimated model as:

ÃIitKi;t¡1

!= ¹t + ½1

ÃIi;t¡1Ki;t¡2

!+ !0¢yit + !1¢yi;t¡1 + !2¢y

2it (3.7)

+!3(¾it¢yit) + Ã0

ÃCitKi;t¡1

!

+µ(k ¡ y)i;t¡2 + ´i + 't + "it

As noted above, the analysis of investment under uncertainty with partial

irreversibility of investment decisions unambiguously predicts !0 > 0 and more

importantly !3 < 0. For …rms undertaking positive investment this approach also

predicts !2 > 0. These are the predictions we test in our empirical analysis.

4. Data Description

4.1. Stylized Facts

The dataset we use comes from a high-quality survey conducted annually by the

Bank of Italy. The survey covers a random sample of …rms representative of the

Italian manufacturing sector from 1984 to 19982. Table A1 provides a detailed

breakdown of the sample according to the type of ownership, location, industrial

sub-sectors, and the size of …rms surveyed. The median …rm size - 276 employees

- is relatively small, and 51% of the …rms in the sample employ between 100 and

499 employees. Most …rms belong to groups while only a tiny proportion of them

- 0.6% - is quoted on the stockmarket for the years when such information is

available. In order to investigate the investment dynamics at the micro level, we

have merged the information from this source with that from a balance-sheets

databank. Although introducing this additional information entails the loss of

2See the Data Appendix for a more detailed description of the dataset.

12

3,800 out of the initial 14,854 observations, the resulting sub-sample inherits the

main properties described in Table A1.

In Table 3a we report the frequency of zero investment episodes both for

total investment and for its breakdown into buildings, plant & machinery and

transportation. The data refer to all the …rms in our sample from 1991, since

such a breakdown is not available for earlier years.

Taking …rm size as a proxy for the number of capital lines within each business,

it may be noticed that the frequency of zero investment episodes tends to be higher

for smaller …rms, and is higher when considering investment data disaggregated

by type of asset.

In selected years, the number of plants operating within each …rm is available

and Table 3b reports a breakdown of investment expenditures according to the

number of plants in 1995. Although this table refers to a cross-section only, it

appears to con…rm the previous insight that the frequency of zero investments is

higher among individual types of capital goods as well as less aggregated produc-

tion units. These …ndings suggest that the investment behavior of the …rms in

our sample may be consistent with the predictions of threshold-rule-based poli-

cies. However the importance of aggregation needs to be taken into account when

testing these predictions using total investment spending, even with data at the

…rm level.

Table 3a. Frequency of Zero Investment Episodes (%)

13

No. Employees Building Machinery Transport. Tot. Inv.

50-99 54.18 1.55 41.59 0100-199 41.25 0.59 30.77 0200-499 29.73 0.68 21.99 0500-999 25.69 0 19.33 0

1,000 or more 16.22 0 14.29 0Note: Data refer to the period from 1991 to 1998 and count 5493 observations.

Table 3b. Frequency of Zero Investment Episodes (%)Plants Building Machinery Transport. Tot. Inv.

1 41.31 0.82 26.58 02 or more 25.41 0 13.58 0

Note: Data refer to 1995 only and count 791 observations.

4.2. Measuring Uncertainty

We derive measures of …rm uncertainty based on ex-post errors in managers’ ex-

pectations. Firms face several sources of uncertainty related not only to wages and

prices (productivity and demand shocks) but also to the cost of raw materials, ex-

change rates, technology, consumer tastes and government policies. We construct

two measures based on the one-year ahead investment expectations of …rms’ man-

agers collected in the survey. The former is the mean of a …rm’s absolute one-step

ahead forecast error in respect to the investment rate:

unciabsi =1

T ¡ 1TX

t=2

¯̄¯̄¯ItKt¡1

¡ Et¡1ÃItKt¡1

!¯̄¯̄¯ (4.1)

The …rst observation is lost since a forecast error is not available for the …rst

year in which a …rm enters the sample. The absolute-sign operator ensures that

positive as well as negative forecast errors are equally weighted.

14

The second measure is analogous to the previous one, except that it assigns

additional weight to relatively large unanticipated shocks by squaring all the fore-

casting errors:

uncimsqi =1

T ¡ 1TX

t=2

"ItKt¡1

¡ Et¡1ÃItKt¡1

!#2(4.2)

The survey allows us to construct similar measures based on forecast errors in

managers’ expectations of employment growth:

unceabsi =1

T ¡ 1TX

t=2

jet ¡ Et¡1 (et)j

where et is the rate of growth in a …rm’s employment in year t.

Similarly:

uncemsqi =1

T ¡ 1TX

t=2

[et ¡ Et¡1 (et)]2 (4.3)

5. Econometric Analysis

5.1. Estimation

The speci…cations above lead to the estimation of a dynamic panel data model.

Arellano and Bond (1991) have developed a GMM estimator to account for the en-

dogeneity of current-dated explanatory variables and for unobserved …rm-speci…c

e¤ects. Their …rst-di¤erenced GMM estimator relies on equations in …rst dif-

ferences from which …rm-speci…c e¤ects are eliminated; regressors can then be

instrumented using lagged endogenous variables provided that the time-varying

component of the model’s residuals exhibits no serial correlation. Arellano and

15

Bond (1991) also provide useful tests for inspecting the degree of serial correla-

tion in the residuals, whose results we report in Table 4. However, Blundell and

Bond (1998) have found that in dynamic panel data models where the individual

series are reasonably persistent and where the number of time-series observations

is relatively small, lagged levels of the series provide only weak instruments for

variables in …rst di¤erences and the resulting GMM estimates exhibit large …nite

sample biases. Their extended GMM estimator makes use of lagged di¤erences of

endogenous variables as instruments for equations in levels, in addition to lagged

levels of endogenous variables as instruments for equations in …rst di¤erences (see

also Arellano and Bover (1995)). Monte-Carlo simulations have shown that this

extended GMM estimator yields substantial gains in the precision of parameter

estimates and potentially dramatic reductions in the …nite sample bias, provided

that these additional instruments are valid. This can be tested using standard

tests of overidentifying restrictions.

While we report the whole set of results based on this extended GMM estima-

tor, we have also checked their robustness against alternative estimators such as

Within Groups, OLS and …rst-di¤erenced GMM. The validity of the instruments

is assessed by means of a Sargan test of overidentifying restrictions. Our preferred

results treat both sales and cash ‡ow variables as predetermined, with the set of

instruments reported in detail in the note to Table 4. The reported results are the

one-step GMM estimates and heteroskedasticity-robust standard errors computed

by DPD98 for Gauss (although a more e¢cient two-step GMM estimator is also

available, its asymptotic standard errors are a¤ected by a …nite sample bias).

16

5.2. Main Results

In reporting our results we start from the simplest linear speci…cation and com-

ment upon various extensions of it. Column (1) contains results for the most basic

speci…cation that does not take into account …nancial constraints (Ct=Kt¡1), nor

non-linearity (¢y2t ) nor the interaction terms between real sales growth and our

measures of uncertainty.

The point estimates on current and lagged growth in real sales are along the

lines of the results reported by Bond, Harho¤ and Van Reenen (1999) for German

…rms. The coe¢cient on the error correction term (-0.14) is correctly signed.

The hypothesis of constant returns to scale was not rejected by the data at

conventional signi…cance levels and is imposed throughout. Diagnostic test results

are very satisfactory, o¤ering no evidence of second order-serial correlation in the

…rst-di¤erenced residuals and the Sargan statistic does not reject the overidenti-

fying restrictions.

Financial constraints proxied by a cash ‡ow term are found to have a more

modest e¤ect in this sample of Italian …rms (the point estimate is 0.066) than the

one Bond, Harho¤ and Van Reenen (1999) estimated using UK data, although its

incluion does have the e¤ect of lowering the estimates on all the other coe¢cients

in the model. The inclusion of a lagged cash ‡ow term (Ct¡1=Kt¡2) generated a

point estimate that was not signi…cant at conventional levels.

The squared sales growth rate term allows us to test the null hypothesis of a

linear accelerator e¤ect against the alternative of non-linear dynamics predicted

by the real options model. The coe¢cient on this quadratic term is both large

17

(point estimate of 0.78) and signi…cantly di¤erent from zero (p-value of 0.002),

indicating a strictly convex response of investment to demand shocks.

Finally, the inclusion of the uncertainty interaction terms allows us to test the

null hypothesis of a common response of investment to demand shocks for …rms

facing high or low levels of measured uncertainty. Again, from our results this null

hypothesis is clearly rejected. All four measures of uncertainty exert a signi…cant

short run e¤ect on …rms’ investment behaviour, with a weaker resonse to demand

shocks at higher levels of uncertainty, exactly the e¤ect predicted by the partial

irreversibility model. The average squared forecast errors of employment and

investment, which give more weight to larger errors, are both found to have a

larger impact on investment dynamics than the average absolute forecast errors.

The coe¢cients attached to the interaction terms based on the forecast errors for

employment are estimated with more precision and exhibit p-values of 0.0003 and

0.003, while the corresponding p-values for the interaction terms based on the

forecast errors for investment are 0.03 and 0.02 respectively.

The theory of investment under partial irreversibility predicts that …rms’ short

term investment policy becomes less responsive to demand shocks at higher levels

of uncertainty since a more cautious approach has a higher pay-o¤. This theoret-

ical prediction is supported by our empirical analysis. The policy implications of

this result are potentially important: investment reacts more sluggishly to policy

interventions when …rms operate in a more uncertain environment. The level of

uncertainty is therefore important in predicting the short run e¤ects of policy

interventions. We believe this result on the e¤ect of uncertainty on investment

18

dynamics can also shed light on why empirical estimates of mainstream invest-

ment equations often fail to perform satisfactorily: failure to include interactions

between uncertainty and output growth, and non-linear terms in output growth,

in empirical models of the investment rate may lead to unstable parameter esti-

mates.

Table 4. Investment Equations

(1) (2) (3) (4) (5) (6) (7)It¡1=Kt¡2 0:0567

0:02640:07920:0435

0:07410:0448

0:07820:0444

0:07430:0444

0:07100:0420

0:07350:0418

¢yt 0:15150:0264

0:10680:0303

0:09200:0323

0:1100:0329

0:18590:0459

0:10390:0290

0:16290:0418

¢yt¡1 0:12590:0339

0:09130:0359

0:08840:0352

0:09340:0331

0:09690:0311

0:08490:0355

0:08410:0349

(k ¡ y)t¡2 ¡0:13860:0287

¡0:09940:0275

¡0:09830:0277

¡0:09140:0268

¡0:09490:0270

¡0:10180:0267

¡0:09730:0271

Ct=Kt¡1 ¡ 0:06620:0275

0:06810:0276

0:07340:0266

0:07090:0259

0:07830:0262

0:07610:0249

¢y2t ¡ ¡ 0:78140:2550

0:77510:2458

0:78830:2487

0:65040:2448

0:66950:2489

uncemsq ¤¢yt ¡ ¡ ¡ ¡0:15910:0438

¡ ¡ ¡unceabs ¤¢yt ¡ ¡ ¡ ¡ ¡0:4094

0:1384¡ ¡

uncimsq ¤¢yt ¡ ¡ ¡ ¡ ¡ ¡0:11970:0566

¡unciabs ¤¢yt ¡ ¡ ¡ ¡ ¡ ¡ ¡0:4300

0:1880

Sargan (p) 0:845 0:860 0:925 0:941 0:919 0:901 0:735LM (1) ¡8:601 ¡8:614 ¡8:786 ¡8:740 ¡8:694 ¡8:887 ¡8:859LM(2) ¡0:789 ¡0:821 ¡0:446 ¡0:474 ¡0:575 ¡0:366 ¡0:273

Observations 1837 1837 1837 1837 1837 1837 1837Firms 286 286 286 286 286 286 286

Notes: Asymptotically robust standard errors are reported below the coe¢cients;estimation by GMM-SYSTEM using DPD98 package one-step results; full set of time-dummies included, results available upon request; ’Sargan’ is a Sargan-Hansen testof overidentifying restrictions (p-value reported); ’LM(k)’ is the test statistic for thepresence of k-th order serial correlation in the …rst-di¤erenced residuals, distributedN(0,1) under the null; in column (1) instruments are It¡2=Kt¡3, It¡3=Kt¡4, yt¡1, yt¡2,

(k¡y)t¡2, (k¡y)t¡3 in the di¤erenced equations, ¢³It¡1Kt¡2

´, and ¢(k¡y)t¡2 and ¢yt

in the levels; in column (2) we also include Ct¡1=Kt¡2, Ct¡2=Kt¡3 in the di¤erenced

19

equations; in column (4) to (8) we include the appropriate interaction uncertainty vari-able lagged one and two periods in the di¤erenced equations and the current change inthis interaction term in the levels equations.

6. Conclusion

In this paper we have tested the predictions of a model of investment under

partial irreversibility, using data on Italian …rms. Following Bloom, Bond and

Van Reenen (2001), we emphasize that these models predict a slower response of

investment to demand shocks at higher levels of uncertainty, as well as a strictly

convex response of investment to current demand shocks. We use data on …rms’

expectations of future investment and employment growth, available in the Survey

of Investment in Manufacturing, to construct measures of uncertainty based on

the average size of their forecast errors. We …nd that investment responds more

slowly to real sales growth for …rms which face a higher level of uncertainty, and

we also …nd evidence of the predicted non-linear response of investment to real

sales growth.

These …ndings have important implications for the e¤ects of monetary and

…scal policies on …rms’ investment spending, suggesting that a given demand

stimulus will have weaker e¤ects in the short run at higher levels of uncertainty.

Our results suggest that these heterogeneous and non-linear dynamics may also

be important in developing stable models of investment for more aggregated data.

However it should be noted that the e¤ect of uncertainty on capital accumulation

in the longer run, which is theoretically ambiguous in this class of models, is

20

not identi…ed from our empirical analysis. Further research using time-varying

measures of uncertainty will be required to investigate this long run relationship.

21

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24

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25

8. Data Appendix

The dataset employed in this paper has resulted from the merger of two sources:

the Bank of Italy Survey of Investment in Manufacturing (SIM) and the Company

Accounts Data Service (CADS).

8.1. The Survey of Investment in Manufacturing

The Bank of Italy carries out an annual survey of realized and planned invest-

ments in …xed capital among a randomly-selected sample of …rms representative

of the manufacturing industry. Whilst the basic version of the survey was …rst

carries out in the 1970s, results have been available in electronic format since the

1984 edition. The unit surveyed is the …rm and the relevant population refers

to businesses with more than 50 employees and operating in the manufacturing

industry, though excluding the energy sector which has only been included in the

survey since the 1999 edition. The sample is strati…ed on the basis of …rm size,

geographical location and sector of activity according to the joint frequency dis-

tribution compiled by the ISTAT, the Italian National Statistics Institute. While

the number of employees is taken as the measure of …rm size, the sector of activ-

ity refers to the ISTAT three-digit ATECO-91 classi…cation consistent with the

NACE-CLIO international standards. The geographical location is taken to be

the region in which the …rm has established its legal headquarters.

Despite its name, the survey collects data on a number of variables besides

investment: in addition to indicating the reasons for not fully realizing their

investment plans, …rms are asked about employment …gures and their expected

26

growth, the number of e¤ective hours worked, the utilization of their technical

productive capacity and the change in capacity utilization over the year, their

total and export turnover and the change in the price of the goods they produce.

In the recent years, …rms have also been surveyed about their expected turnover,

the expected change in their goods prices, their ownership structure, and have

been asked to report whether they are listed on a stockmarket or belong to a

group. For selected years, data on the number of plants operated by each …rm is

also available. Starting from 1992, an additional section has been added to the

survey. Firms have been surveyed about e-commerce (1999), labor …ring costs

(1998), capital stock and foreign investments (1997), pricing policies and market

structure (1996), technological change (1995), wage bargaining (1994), product

demand expectations (1993) and their ownership structure (1992).

The survey is carried out by means of interviews by highly-trained Bank of

Italy o¢cials who have normally established long term relationships with …rms

managers. Questionnaires are sent out by the end of December of the year the

survey refers to and are then collected by April of the following year at the lat-

est. Responses are carefully scrutinised by specialized teams within the Bank.

These teams also check with …rms any possible inconsistencies arising from their

responses. In the survey, …rms are also asked whether a major corporate event

has occurred in the year (a merger or an acquisition etc.) and to report data on

a basis consistent with the previous year.

The number of businesses surveyed each year is around 1,000.

27

8.2. The Company Accounts Data Service

The CADS is provided by Centrale dei Bilanci, an institution owned by the Bank

of Italy and a consortium of commercial banks. The dataset comprises roughly 800

items from income statements, balance-sheets and other non accounting sources of

about 40,000 …nancial and non-…nancial …rms. The data is aggregated in order to

ensure comparability across …rms and is available on an annual basis since 1981.

The sample is non random since a …rm enters the dataset after applying for a loan

to one of the banks owning Centrale dei Bilanci. Matching the CADS and the

SIM datasets results in an unbalanced panel of roughly 800 …rms per year (Table

X).

8.3. Estimation of Capital Stocks

Capital stocks have been estimated through a perpetual inventory method:

P It Kt = (1 ¡ ±)P It¡1Kt¡1(PIt =P

It¡1) +P

It It ¡ P It DISPt

where:

Kt = End-of-Period Real Capital Stock

P It = Price of Investment Goods

It = Real Gross Investment

DISPt = Real Revenues from Sales of Investment Goods

± = Depreciation Rate

We have assumed that capital depreciates at an annual rate of 8% and the

benchmark capital stock is on average two years old.

28

8.4. Output

Sales de‡ated by the Producer Price Index have been used as proxy for real output.

8.5. Cash Flow

Current cash ‡ow is available from Centrale dei Bilanci.

29

Table A1. Comparison of the SIM and SIM-CADS SamplesSIM-CADS SIM

Abs. Freq. Rel. Freq. Abs. Freq. Rel. Freq.Sector:Private 10448 0.94 13988 0.94State-owned 606 0.06 885 0.06Location:North 7832 0.71 10405 0.70Centre 1911 0.17 2563 0.17South 1311 0.12 1905 0.13Industrial Sector:Metallurgy 1142 0.10 1664 0.11Nonmetallic Mineral Products 903 0.08 1176 0.08Chemical Products 990 0.09 1274 0.09Machinery 1717 0.16 2254 0.15Electrical Goods 941 0.09 1313 0.09Trains, Ships, Airplanes & Motor Vehicles 738 0.07 1002 0.07Food Products, Beverage & Tobacco 981 0.09 1416 0.09Clothing and Textile 1641 0.15 2238 0.15Leather and Footwear 441 0.04 599 0.04Timber and Furniture 170 0.02 234 0.02Paper, Products of Printing & Publishing 551 0.05 680 0.05Rubber and Plastic Goods 475 0.04 602 0.04Other Manufacturing Goods 364 0.02 421 0.02Size:50-99 Employees 2012 0.18 2925 0.20100-199 Employees 2441 0.22 3246 0.22200-499 Employees 3175 0.29 4203 0.28500-999 Employees 1703 0.15 2219 0.111000 and More Employees 1723 0.16 2280 0.19Firm size (employees median) 276 266

Listed Firms (*) 32 0.04 36 0.04Firms belonging to a group (*) 502 0.60 627 0.60

Total Sample 11054 14876

Notes: (*)=reference year: 1997. Total sample: 830 and 1003 …rms for the CADS-SIM and SIM samples, respectively.

30