Love_2006_pvar (1)

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The Quarterly Review of Economics and Finance 
46 (2006) 190–210 
Financial development and dynamic investment 
behavior: Evidence from panel VAR 
Inessa Love a , Lea Zicchinob,∗ 
a World Bank, Rearch Department—Finance Group, 1818 Hst, NW, MC3-300, 
Washington, DC 20433, United States 
b  Bank of England, Monetary Instruments and Markets Division, HO-2, 
Threadneedle Street, London EC2R 8AH, UK 
Received 14 April 2004; received in revid form 31 October 2005; accepted 4 November 2005 
Abstract 
We apply vector autoregression (VAR) to firm-level panel data from 36 countries to study the dynamic 
relationship between firms’ financial conditions and investment. By using orthogonalized impul-respon 
functions we are able to parate the ‘fundamental factors’ (such as marginal profitability of investment) 
北京交通大学爆炸from the ‘financial factors’ (such as availability of internal finance) that influence the level of investment. We 
find that the impact of financial factors on investment, which indicates the verity of financing constraints, 
is significantly larger in countries with less developed financial systems. Our finding emphasizes the role of 
financial development in improving capital allocation and growth. 
© 2006 Board of Trustees of the University of Illinois. All rights rerved. 
my love歌词Keywords:  Financial development; Vector autoregression; Dynamic investment behavior 
1.  Introduction 
Unlike the neoclassical theory of investment, the literature bad on asymmetric information 
emphasizes the role played by moral hazard and adver lection problems in a firm’s decision 
to invest in physical and human capital. The prence of asymmetric information means that the 
classical dichotomy between real and financial variables may no longer hold. Financial variables 
can have an impact on real variables, such as the level of investment and the real interest rate, as well 
as propagate and amplify the effects of exogenous shocks to the economy. For example, Bernanke 
∗ Corresponding author. Tel.: +44 20 7601 5212; fax: +44 20 7601 3217. 
E-mail address: lea.uk (L. Zicchino). 
1062-9769/$ – e front matter © 2006 Board of Trustees of the University of Illinois. All rights rerved. 
doi: 10.1016/j.qref.2005.11.007 
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I. Love, L. Zicchino / The Quarterly Review of Economics and Finance 46 (2006) 190–210              191 
and Gertler (1989) show that a firm’s
net worth (a financial variable) can be ud as collateral in 
order to reduce the agency cost associated with the prence of asymmetric information between 
典故的意思
lenders and borrowers. In this model, firms’ investment decisions are not only dependent on the 
prent value of future marginal productivity of capital, as the q-theory predicts, but also on the 
level of collateral available to the firms when they enter a loan contract. 
Since  economists  started  to  look  at  real  phenomena  abstracting  from  the  Arrow-Debreu 
framework with its frictionless capital markets, a vast literature has been developed on the rela- 
tionship between investment decisions and firms’ financing constraints (e Hubbard, 1998, for 
a  review).  Even  though  asymmetric  information  between  borrowers  and  lenders  may  be  not 
the only source of imperfection in the credit markets, firms em to prefer internal to external 
finance to fund their investments. This obrvation leads to the prediction of a positive relation- 
ship between investment and internal finance. The first study on panel data by Fazzari, Hubbard, 
and  Peterson  (1988)  finds  that,  after  controlling  for  investment  opportunities  with  Tobin’s  q, 
changes in net worth have a greater impact on investment by firms with higher costs of external 
financing. 
The link between the cost of external financing and investment decisions not only sheds light 
on the dynamics of business cycles but also reprents an important element in understanding 
economic development and growth. For instance, in the prence of moral hazard in the credit 
market, firms that need a bank loan may be induced to undertake risky investment projects with 
英语词典大全low expected marginal productivity. This corporate decision affects the growth path of the econ- 
omy, which may even fall into a poverty trap (e Zicchino, 2001). Rajan and Zingales (1998), 
Demirguc-Kunt and Maksimovic (1998) and Wurgler (2000), among others, have investigated the 
link between finance and growth by asking whether underdeveloped legal and financial systems 
hippcould prevent firms from investing in potentially profitable growth opportunities. Their empirical 
results show that an active stock market, developed financial intermediaries and the respect of 
legal norms are determinants of economic growth. 
Estimation  of  the  relationship  between  investment  and  financial  variables  is  challenging 
becau it is difficult for an econometrician to obrve firms’ net worth and investment oppor- 
tunities.  In  theory,  the  measure  of  investment  opportunities  is  the  prent  value  of  expected 
future profits from additional capital investment, or what is commonly called marginal q. This 
is  the  shadow  value  of  an  additional  unit  of  capital  and,  under  certain  conditions,  it  can  be 
shown  to  be  a  sufficient  stati
stic  for  investment  (Hayashi,  1982).  In  other  words,  it  is  the 
‘fundamental’ factor that determines investment policy of profit-maximizing firms in efficient 
markets. The difficulty in measuring marginal q, which is not obrvable, results in low explana- 
tory power of the q-models and, typically, entails implausible estimates of the adjustment cost 
parameters. 
Another challenge is finding an appropriate measure for the ‘financial’ factors that enter the 
investment equation in models with capital markets imperfections. A widely ud measure for 
the availability of internal funds is cash flow (current revenues less expens and taxes, gen- 
erally scaled by capital). However, cash flow is likely to be correlated with future investment 
profitability.2  This makes it difficult to distinguish the respon of investment to the ‘fundamen- 
1 See Whited (1998) and Erickson and Whited (2000) for a discussion of the measurement errors in investment models. 
Also e Schiantarelli (1996) and Hubbard (1998) for a review on methodological issues related to investment models 
考研专业课考试时间
with financial constraints. 
2 For example, the current realization of cash flow would proxy for future investment opportunities if the productivity 
shocks were positively rially correlated. 
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192          I. Love, L. Zicchino / The Quarterly Review of Economics and Finance 46 (2006) 190–210 
tal’ factors, such as marginal profitability of capital, and ‘financial’ factors, such as net worth (e 
Gilchrist and Himmelberg, 1995, 1998, for further discussion of this terminology). 
In this paper we u the vector autoregression (VAR) approach to overcome this problem and 
isolate the respon of investment to financial and fundamental factors. Specifically, we focus 
on the orthogonalized impul-respon functions, which show the respon of one variable of 
interest  (i.e.  investment)  to  an  orthogonal  shock  in  another  variable  of  interest  (i.e.  marginal 
productivity or a financial variable). By orthogonalizing the respon we are able to identify the 
effect of one shock at a time, while holding other shocks constant. 
We u firm-level panel data from 36 countries to study the dynamic relationship between firms’ 
akashicfinancial conditions and investment levels. Our main interest is to study whether the dynamics of 
investment are different across countries with different levels of development of financial markets. 
We argue that the level of financial development in a country can be ud as an indication of the 
different  degrees  of  financing  constraints  faced  by  firms.  After  controlling  for  the  shocks  to 
‘fundamental’ factors, we interpret the respon of investment to ‘financial’ factors as evidence 
of financing constraints an
d we expect this respon to be larger in countries with lower levels of 
financial development. To test this hypothesis we divide our data in two groups according to the 
degree of financial development of the country in which they operate. We document significant 
differences in the respon of investment to ‘financial’ factors for the two groups of countries. 
Furthermore, splitting the sample bad on different indicators of economic development does 
bus metro walknot produce significant differences, supporting our claim that the level of financial  development 
is the main determinant of financing constraints. 
We believe our paper contributes to a number of strands in the recent financial economics liter- 
ature. We contribute to the literature on financial constraints and investment in veral ways. First, 
by using vector autoregressions on panel data we are able to consider the complex relationship 
between investment opportunities and the financial situation of the firms, while allowing for a 
firm-specific unobrved heterogeneity in the levels of the variables (i.e. fixed effects). Second, 
thanks to a reduced-form VAR approach, our results do not rely on strong assumptions that are 
necessary in models that u the q-theory of investment or Euler equations. Third, by analyzing 
orthogonalized impul-respon functions we are able to parate the respon of investment to 
shocks coming from fundamental or financial factors. 
We  also  contribute  to  the  finance  and  growth  literature  by  prenting  new  evidence  that 
investment in firms operating in financially underdeveloped countries exhibits dynamic patterns 
consistent with the prence of financing constraints. Our paper also adds to the recent work that 
ud dynamic panel-data techniques to argue that there is a casual link between financial develop- 
ment and growth (e, for example, Beck and Levine, 2004). While most of the previous studies 
relied on country-level data, our paper us firm-level data to demonstrate how the link between 
finance and growth operates on the level of the firm and to provide additional evidence on the 
channels behind this link. Specifically, we find that financial development has an immediate effect 
on efficient allocation of capital via investment that follows the most productive us of capital. 
Our finding is also consistent with the evidence prented by Beck, Demirguc-Kunt, Levine, and 
Maksimovic (2001) who found that it is easier for firms’ to access external financing in countries 
with a higher level of overall financial ctor development. 
Our paper also adds to the recent debate on bank-bad versus market-bad financial systems 
(e, for example, Demirguc-Kunt and Levine, 2001a, b, and Beck and Levine (2002), among 
others). This literature demonstrated that despite conflicting theoretical predictions, there is no 
empi
rical evidence of the relationship between financial structures and economic growth. How- 
ever, the literature has found that it is the overall financial development that helps in explaining 
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I. Love, L. Zicchino / The Quarterly Review of Economics and Finance 46 (2006) 190–210            193 
cross-countries differences in economic performance. Our findings are consistent with this liter- 
ature and expand the range of real effect previously studied to include the micro-level evidence 
of the effect of financial development on investment behavior and capital allocation. 
Our paper is also related to Gilchrist and Himmelberg (1995, 1998), who were the first to 
analyze the relationship between investment, future capital productivity and firms’ cash flow with 
a panel-data VAR approach. They u a two-stage estimation procedure to obtain measures of 
what they call ‘fundamental’ q and ‘financial’ q. The factors are then substituted in a structural 
model of investment, which is a transformation of the Euler-equation model. Unlike Gilchrist 
and  Himmelberg,  we  do  not  estimate  a  structural  model  of  investment,  but  instead  study  the 
unrestricted reduced-form dynamics afforded by the VAR (which is in effect the first-stage in 
their estimation). Gallegati and Stanca (1999) also investigate the relationship between firms’ 
balance sheets and investment by estimating reduced-form VARs on company panel data for UK 
firms. Despite some differences in the specification of the empirical model and the estimation 
methodology, the approach and the results of their paper are similar to ours. However, they do 
not prent an analysis of the impul-respon functions, which we consider to be the main tool 
in parating the role of financial variables in companies’ investment decisions. In addition, the 
distinguishing feature of our paper is the focus on the differences in the dynamic behavior of firms 
in countries with different levels of financial development. 
Finally, our paper is related to Love (2003) who us the Euler-equation approach and shows 
that financing constraints are more vere in countries with lower levels of financial development, 
the same as we find in this paper. However, the interpretation of the results in the previous paper 
is heavily dependent on the assumptions and parameterization of the model, while the approach 
we u here impos the bare minimum of restrictions on parameters and temporal correlations 
economy是什么意思among variables. 
The rest of the paper is as follows: Section 2 prents the empirical specification and the data 
description; Section 3 provides the results of our work; and Section 4 prents our conclusions. 
2.  Empirical methodology 
We u a panel-data vector autoregression methodology. This technique combines
the tra- 
ditional  VAR  approach,  which  treats  all  the  variables  in  the  system  as  endogenous,  with  the 
panel-data approach, which allows for unobrved individual heterogeneity. We specify a first- 
order VAR model as follows: 
z  = Γ    + Γ  z      + f  + d      + e                                                                (1) 
it    0      1  it −1    i    c,t    t 
where zt  is either a three-variable vector  {SKB, CFKB, IKB} or a four-variable vector {SKB, 
CFKB, IKB, TOBINQ}; SKB, sales to capital ratio, is our proxy for the marginal productivity of 
capital,3  IKB is the investment to capital ratio, which is our main variable of interest, CFKB is 
cash flow scaled by capital, and TOBINQ is ‘Tobin’s q’, measured as market value of asts over 
book value of asts. 
In this model sales to capital ratio and Tobin’s q reprent ‘fundamental’ factors, i.e. factors that 
capture the marginal productivity of capital. In the abnce of market frictions, positive shocks to 
3 See  Gilchrist  and  Himmelberg  (1998)  for  a  derivation  of  the  ratio  of  sales  to  capital  as  a  measure  of  marginal 
wto什么意思productivity of capital. 
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194            I. Love, L. Zicchino / The Quarterly Review of Economics and Finance 46 (2006) 190–210 
the fundamental factors should lead to an increa in investment as firms will take advantage 
of better investment opportunities. 
Cash flow is commonly ud in investment models as an indicator for internally available 
funds (e Hubbard, 1998, for a review). In our model, we consider cash flow also as a proxy 
for ‘financial factors’.4  Our analysis is implicitly bad on an investment model in which, after 
controlling for the marginal profitability, the effect of the financial variables on investment is 
interpreted as evidence of financing constraints.5 We do this by relying on the orthogonalization 
of impul respons. Becau the shocks are orthogonalized, i.e. ‘fundamentals’ are kept constant, 
the impul respon of investment to cash flow isolates the effect of the ‘financial’ factors. We u 
this orthogonalized respon of investment to ‘financial factors’ as a measure of market frictions 
and financing constraints. 
The impul-respon functions describe the reaction of one variable to the innovations in 
another  variable  in  the  system,  while  holding  all  other  shocks  equal  to  zero.  However,  since 
the actual variance–covariance matrix of the errors is unlikely to be diagonal, to isolate shocks 
to  one  of  the  variables  in  the  system  it  is  necessary  to  decompo  the  residuals  in  a  such  a 
way that they become orthogonal. The usual convention is to adopt a particular ordering and 
allocate any correlation between the residua

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