Massachusetts Institute of Technology
Department of Economics
Working Paper Series
Volatility & Growth: Credit Constraints and
Productivity-Enhancing Investment
Philippe Aghion
George-Marios Angeletos
Abhijit Banerjee
Kalina Manova
Working Paper 05-15
April 30, 2005
Room E52-251
50 Memorial Drive
Cambridge, MA 02142
This paper can be downloaded without charge from the
Social Science Research Network Paper Collection at
http://ssrn.com/abstract=719772
Volatility and Growth:
Credit Constraints and Productivity-Enhancing Investment∗
Philippe Aghion George-Marios Angeletos Abhijit Banerjee Kalina Manova
Harvard and NBER
MIT and NBER
MIT and NBER
Harvard
First draft: October 2003. This version: April 2005.
Abstract
We examine how credit constraints affect the cyclical behavior of productivity-enhancing investment and thereby volatility and growth. We first develop a simple growth model where firms
engage in two types of investment: a short-term one and a long-term productivity-enhancing
one. Because it takes longer to complete, long-term investment has a relatively less procyclical
return but also a higher liquidity risk. Under complete financial markets, long-term investment is countercyclical, thus mitigating volatility. But when firms face tight credit constraints,
long-term investment turns procyclical, thus amplifying volatility. Tighter credit therefore leads
to both higher aggregate volatility and lower mean growth for a given total investment rate.
We next confront the model with a panel of countries over the period 1960-2000 and find that
a lower degree of financial development predicts a higher sensitivity of both the composition
of investment and mean growth to exogenous shocks, as well as a stronger negative effect of
volatility on growth.
JEL codes: E22, E32, O16, O30, O41, O57.
Keywords: Growth, fluctuations, business cycle, credit constraints, amplification, R&D.
∗
For helpful comments and discussions, we thank Daron Acemoglu, Philippe Bacchetta, Robert Barro, Olivier
Blanchard, V.V. Chari, Bronwyn Hall, Peter Howitt, Olivier Jeanne, Patrick Kehoe, Ellen McGrattan, Pierre Yared,
Klaus Walde, Iván Werning, and seminar participants in Amsterdam, UC Berkeley, ECFIN, Harvard, IMF, MIT, and
the Federal Reserve Bank of Minneapolis. Special thanks to Do Quoc-Anh for excellent research assistance. Email
addresses: p_aghion@harvard.edu, angelet@mit.edu, banerjee@mit.edu, manova@fas.harvard.edu.
1
Introduction
The modern theory of business cycles gives a central position to productivity shocks and the role of
financial markets in the propagation of these shocks; but it takes the entire productivity process as
exogenous. The modern theory of growth, on the other hand, gives a central position to endogenous
productivity growth and the role of financial markets in the growth process; but it focuses on trends,
largely ignoring shocks and cycles.1
The goal of this paper is to build a bridge between the two approaches; to propose and, in
a limited way, test a theory of endogenous productivity growth that gives a central position to
uncertainty. At the heart of our theory is a propagation mechanism — how exogenous shocks
generate endogenous productivity movements — and its interaction with financial markets.
The first part of the paper develops a model that focuses on the cyclical behavior of the composition of investment as the main propagation channel; this choice is motivated by facts discussed
later. Entrepreneurs engage in two types of investment activity: short-term investment takes relatively little time to build and generates output relatively fast; long-term investment takes more
time to complete but contributes more to productivity growth.
With perfect credit markets, investment choices are dictated merely by an opportunity-cost
effect. As long as short-term returns are more cyclical than long-term returns, the opportunity cost
of long-term investment is lower in recessions than in booms.2 The fraction of savings allocated to
long-term investment is therefore countercyclical and, by implication, the endogenous component
of productivity grows faster when coming out of a recession than otherwise.
But with sufficiently imperfect credit markets, long-term investment becomes procyclical and
the business cycle is now amplified. This is not so much because borrowing constraints limit the
ability to invest; in our model the interest rate adjusts in general equilibrium so that neither type
of investment is constrained ex ante. It is rather because tighter constraints imply a higher risk
that long-term investment will be interrupted by some (idiosyncratic) liquidity shock ex post. This
risk in turn reduces the entrepreneurs’ willingness to engage in long-term investment ex ante — and
the more so in recessions, when liquidity is expected to be scarce. Aggregate shocks therefore have
a more pronounced effect on productivity growth when credit markets are less effective.
The second part of the paper confronts the implications of the model with the data. We first
examine whether there is evidence of amplification per se. For that purpose, we look at a panel of
about 60 countries over the 1960-1995 period and use export-weighted commodity price shocks as
our measures of exogenous shocks to the economy. We find the negative impact of an adverse price
shock on growth to be stronger in countries with tighter credit.
1
Though the idea that there is a close connection between productivity growth and the business cycle goes back
at least to Schumpeter, Hicks, and Kaldor in the 1940s-1950s.
2
An opportunity-cost effect of this kind has emphasized by Aghion and Saint-Paul (1998) and more recently by
Barlevy (2004).
1
Financial development in these regressions does not appear to be capturing the role of other
policies or institutions. The interaction between shocks and private credit remains significant
once we control for the interaction between shocks and intellectual property rights, government
expenditures, inflation, and the black-market premium.
We next examine the transmission channel by looking at the response of the rate and the composition of investment to shocks. For that purpose, we proxy the fraction of long-term productivityenhancing investments by the ratio of R&D to total investment. Data availability then limits the
analysis to a panel of 14 OECD countries over the 1973-1999 period. Consistent with the predictions of our model, we find that the composition of investment is more sensitive to shocks when the
level of private credit is lower. On the other hand, and in contrast to the standard credit-multiplier
paradigm (e.g., Bernanke and Gertler, 1989), we find no evidence that the total rate of investment
is more responsive to shocks when private credit lower.
While we are probably the first to look at the effects of shocks on the composition of investment
and thereby on growth in the presence of financial constraints, there is a literature that looks at
the related but distinct question of how volatility affects growth. Most notably, Ramey and Ramey
(1995) find a negative relation between the two in cross-country data; this relation is robust to
various controls, perhaps suggesting a negative causal effect of volatility on growth.3
Such a causal effect of volatility on growth would be consistent with the neoclassical growth
paradigm if volatility discourages the demand for investment more than it encourages the precautionary supply of savings. In an AK economy, for example, the general-equilibrium impact
of aggregate risk on savings and thereby on growth is negative if the elasticity of intertemporal
substitution is higher than one (Jones, Manuelli and Stacchetti, 2000).4
It is however unclear that this is the right explanation for the observed correlation. First, the
impact of volatility persists even after we control for the aggregate investment rate. This is shown
in columns 1-4 of Table 1, which repeat some of Ramey and Ramey’s (1995) basic specifications in
our data set. For example, in the specification that includes initial income, education, and policy
and demographics variables as in Levine et al. (2000), the point estimate of the volatility coefficient
falls from −0.26 to −0.22 when the investment rate is included in the controls. Prima facie, this
finding suggests that the main channel through which volatility affects growth is not the overall
propensity to save — one reason why we chose to focus on the composition of investment.
[insert Table 1 here]
3
Similar evidence is provided by Blattman, Hwang and Williamson (2004), Koren and Tenreyro (2004), and our
own results in Section 5.4. Chatterjee and Shukayev (2005), however, argue that this relation is not always robust to
different regression specifications or country samples.
4
The results in Angeletos (2004) suggest that the relevant threshold for the elasticity of intertemporal substitution
may be quite lower in a neoclassical economy where, unlike in an AK economy, capital is not the only source of
income.
2
Second, the relation may be partly spurious, driven by the effect that financial development — a
factor that Ramey and Ramey did not control for — has on both growth and volatility. This point
is consistent with our model, where tighter credit constraints imply a lower and a more procyclical
long-term investment and therefore a slower and more variable growth process. It is also consistent
with the standard credit-multiplier paradigm, which proposes that financial frictions amplify the
business cycle via their effect on the variability of aggregate investment. But whereas there is
evidence that credit predicts growth and volatility, a first pass of the data shows no indication that
credit predicts the variability of the investment rate.
In our sample, the cross-country correlation between the mean growth rate and the ratio of
private credit to GDP (the measure of financial development usually used in the literature) is 0.40;
the correlation between the volatility of the growth rate and private credit is −0.48. As shown in
columns 5 and 6 of Table 1, the effect of credit on volatility is robust to various controls; the same is
true for mean growth (see Levine, 1997, for a review). By contrast, the correlation between private
credit and the standard deviation of the rate of investment to GDP is nearly zero (only −0.09); and
when in columns 7 and 8 of Table 1 we repeat the same regressions as in columns 5 and 6 now using
the standard deviation of the investment rate as the dependent variable, we find no relationship
between the latter and the quality of the financial sector — another reason why we chose to focus
on the composition rather than the rate of investment as the main transmission channel.
At the end of the empirical section we thus revisit the relation between volatility and growth
in the cross-section. We find that the effect of volatility on growth survives when we control for
the level of financial development, leaving open the possibility that volatility has a causal effect on
growth (or, of course, that there may be some other omitted variable not captured by private credit
and the other controls). In our model, the causal effect can go either direction, partly because the
effect of aggregate volatility on the level of idiosyncratic liquidity risk is ambiguous in general. An
interesting possibility, however, emerges in examples where liquidity risk increases with aggregate
volatility. Higher volatility then discourages long-term investment and slows down growth, and the
more so the tighter the credit constraints. Consistent with this possibility, we find in our sample
that the negative relation between volatility on growth tends to be stronger in countries with lower
financial development. This finding, however, should be taken with caution, for it looses significance
when we instrument volatility with the standard deviation of commodity price shocks.
Related literature. The growth and amplification effects of financial frictions have been
the subject of a large literature, including Bernanke and Gertler (1989), Banerjee and Newman
(1991), King and Levine (1993), Obstfeld (1994), Kiyotaki and Moore (1997), Holmstrom and
Tirole (1998), Aghion, Banerjee and Piketty (1999).5 We depart from this earlier work by focusing
on how liquidity risk interacts with the horizon of investment and how this in turn affects the
5
See Levine (1997) for an excellent review and more references.
3
cyclical composition of investment. Angeletos (2004) also considers how idiosyncratic risk affects
the cyclical allocation of investment, but focuses on private versus public equity.
King and Rebelo (1993), Stadler (1990), and Jones, Manuelli and Stacchetti (2000) analyze
the relation between volatility and growth within the AK class of models, but do not consider the
cyclical behavior of the allocation of investment nor the role of financial markets. Hall (1991), Gali
and Hammour (1991), Aghion and Saint-Paul (1998), and Barlevy (2004) examine the cross-sectoral
allocation of investment, but assume perfect capital markets, thus bypassing the interaction effects
identified here.6
Related are also Caballero and Hammour (1994) and Acemoglu and Zilibotti (1997). The
first paper examines the implications of adjustment costs for volatility and the cleansing effect of
recessions. The second argues that lower levels of income, by constraining the ability to diversify
sector-specific risks, may lead to both higher volatility and lower growth.7 By contrast, this paper
focuses on the interaction of credit constraints and the composition of investment.
The rest of the paper is organized as follows. Section 2 outlines the model. Section 3 analyzes
the composition of investment and Section 4 the implications for growth and volatility. Section 5
contains the empirical analysis. Section 6 concludes.
2
The model
The economy is populated by overlapping generations of two-period-lived agents (“entrepreneurs”),
who are indexed by i and uniformly distributed over the segment [0, 1]. In the first period of her
life, an entrepreneur receives an exogenous endowment of wealth and decides how much to invest
in short-term versus long-term investment. Short-term investment produces at the end of the first
period, whereas long-term investment produces at the end of the second period. In between, a
random liquidity shock is realized, which threatens to reduce the return of long-term investment if
not financed. At the end of the second period, the entrepreneur consumes her total life-time income
and dies. The life-span of an entrepreneur is illustrated in Figure 1 and further explained below.
Productivity and exogenous shocks. Aggregate productivity has two components: an
exogenous and an endogenous one. We denote the endogenous component in period t with Tt
and call it the level of knowledge; the determination of Tt will be described later. The exogenous
component, on the other hand, is denoted by at and is assumed to follow a Markov process with
support [a, a] ⊆ R+ , unconditional mean normalized to 1, and conditional mean Et−1 at = aρt−1 ,
where ρ ∈ (0, 1) parametrizes the persistence in exogenous productivity.
6
Francois and Lloyd-Ellis (2003), on the other hand, consider a Schumpeterian growth model in which cycles are
generated by firms’ incentives to synchronize their innovations, as in Shleifer (1986).
7
Koren and Tenreyro (2004), however, argue that, contrary to the portfolio-diversification approach, less developed
countries specialize in sectors with relatively higher, not lower, risks.
4
period t+1
period t
day
day
night
• productivity at is realized
• kit returns atf(kit)
• productivity at+1 is realized
• period-t agents are born
• liquidity shock cit is realized
• agents borrow and lend to
invest in kit and zit
• agents borrow and lend to
meet liquidity shocks
• zit returns at+1q(zit) if liquidity
has been met, 0 otherwise
• period-t agents consume and die
• period-t+1 agents are born …
Figure 1: The life of an entrepreuneur.
Short-term and long-term investment. Consider an entrepreneur born in period t. In
the beginning of life, the entrepreneur receives an endowment of wealth, Wti , and decided how to
allocate it between short-run investment, Kti , long-term investment, Zti , and savings in the riskless
bond, Bti . To ensure a balanced-growth path, we assume that the initial endowment and the costs
of short-term and long-term investments are proportional to Tt , and denote with wti = Wti /Tt ,
kti = Kti /Tt , zti = Zti /Tt , and bit = Bti /Tt the “detrended” levels of wealth, short-term investment,
long-term investment, and bonds holdings. We also assume that wti = w for some constant w > 0,
which effectively fixes the supply of savings.8 The initial budget thus reduces to
kti + zti + bit ≤ w.
(1)
Short-term investment takes only one step to complete, namely the initial investment Kti in the
beginning of the first period, and generates output
Πit = at Tt π(kti )
at the end of the same period, where π is a neoclassical production function (i.e., such that π 0 >
0 > π 00 , π 0 (0) = ∞, and π 0 (∞) = 0).
Long-term investment, on the other hand, takes two steps to complete: the initial investment Zti
incurred in the beginning of the first period and an additional random adjustment cost Cti incurred
at the end of the first period. Long-term investment produces
Πit+1 = at+1 Tt q(zti ) + Cti
at the end of the second period if this additional cost has been met, and nothing otherwise, where q
8
Here we are in effect ruling out any influence of the quality of financial markets the volatility of the aggregate
investment rate, which is consistent with the evidence discussed in the introduction. We are also ruling out any
influence on the mean investment rate; this is certainly not true in the data, but it is not important for the paper:
none of the results is affected if we let w be an increasing function of µ.
5
is also a neoclassical production function (q 0 > 0 > q 00 , q 0 (0) = ∞, q 0 (∞) ≤ 0). To ensure a balanced
growth path, we assume that Cti is proportional to Tt and let cit = Cti /Tt be independently identically
distributed across agents and periods, with support [0, c̄], cumulative distribution function (c.d.f) F,
and density f. Unless otherwise stated, we further simplify by assuming that the c.d.f. is isoelastic:
F (c) = (c/c̄)φ , with φ > 0.
Remarks. Note that the return to each type of investment depends on the corresponding
contemporaneous productivity shock (i.e., at for the short-term investment, at+1 for the long-term
one), whereas both depend on the level of knowledge that the entrepreneur learns in the beginning
of his life (i.e., Tt ). The first assumption is essential: together with the assumption that at is meanreverting, it ensures that the return to short-term investment is more cyclical than the return
to long-term investment. The second assumption is less important: assuming that the output of
long-term investment depends on Tt+1 rather than Tt would not change any of the results.9 The
assumption that Πit+1 includes Cti is also inessential: it simply ensures that Cti represents a pure
liquidity shock. That is, since at+1 Tt q(zti ) > 0, it is always optimal for the firm to pay the additional
cost whenever it can, which in turn depends upon the efficiency of credit markets.
There are various interpretations of what the two types of investment and the liquidity shock
represent. For example, the short-term investment might be putting money into one’s current
business, while long-term productivity-enhancing investment may be starting a new business. Or,
the short-term investment may be maintaining existing equipment or buying a machine of the
same vintage as the ones already installed, while the long-term investment is building an additional
plant, investing in R&D, learning a new skill, or adopting a new technology. Similarly, the liquidity
shock might be an extra cost necessary for the new technology to be adapted to domestic market
conditions once the new technology has been adopted; or a health problem which the entrepreneur
needs to overcome or otherwise she won’t be alive to enjoy the fruits of her long-term investment;
or some other idiosyncratic shock that is threatening to ruin the entrepreneur’s business unless she
has enough liquidity to overcome it.10
Entrepreneur’s payoff. The entrepreneur is risk neutral and consumes only in the last period
¡ i
¢
i ], where W i
i
i i+
of her life. Hence, expected life-time utility is simply Et [Wt+1
t
t+1 = Πt + Πt+1 − Ct
(1 + rt )Bti is the entrepreneur’s final-period wealth and
the firm meets its liquidity shock and
i
t
i
t
is an indicator variable such that
i
t
= 1 if
= 0 otherwise. Equivalently, the entrepreneur maximizes
i ], where
Et [wt+1
i
i
≡ Wt+1
/Tt = at π(kti ) + at+1 q(zti ) it + (1 + rt )bit .
wt+1
9
(2)
This would introduce a complementarity in long-term investment across entrepreneurs, which in turn would
increase its countercyclicality under complete markets and its procyclicality under tight constraints.
10
The fact that long-term productivity-enhancing investments — such as setting up a new business, learning a new
skill, adopting a new technology, or engaging in R&D — are largely intangible explains why a relatively large fraction
of the value of such investments may not be tradeable and may therefore be lost in case the liquidity shock is not
met. The assumption that everything is lost is then only for simplicity.
6
Credit markets. Credit markets open twice every period. The “day” market takes place at the
beginning of the period, before the realization of the liquidity or long-term investment adjustment
cost. The “overnight” market takes place at the end of the period, after the realization of the
liquidity cost.
In the day market the entrepreneur can borrow up to m times her initial wealth (m ≥ 0). The ex
ante borrowing constraint can thus be expressed as kti +zti ≤ µw, where µ ≡ 1 +m ≥ 1. Similarly, in
the overnight market, the entrepreneur can borrow up to m times her end-of-current-period wealth,
Xti = at Tt π(kti )+(1 + rt ) Bti , for the purpose of covering the liquidity cost Cti . Thus, the probability
that the entrepreneur will be able to meet the liquidity shock and enjoy the fruits of his long-term
investment is given by
¢
¡ ¢
¡
pit ≡ Pr Cti ≤ µXti = F µxit ,
where xit ≡ Xti /Tt = at π(kti ) + (1 + rt ) bit .
Finally, to simplify the analysis, we assume that wealth cannot be stored during the day, whereas
overnight storage can take place at a one-to-one rate and c̄ ≤ aπ(k̂ (a)), where k̂ (a) is the solution
to aπ 0 (k̂) = aρ q 0 (1 − k̂). The first assumption implies that the “day” interest rate rt will adjust so
that the excess aggregate demand for the riskless bond in the day market is zero; this is equivalent
to imposing the resource constraint
Z
i
(kti + zti ) = w.
(3)
The second assumption ensures that the “overnight” interest rate is zero.
Endogenous growth. To complete the model, we need to describe the endogenous productivity process, that is, the dynamics of Tt . Assuming that the knowledge accumulated by one generation
spills over to the next generation and identifying the knowledge produced by each entrepreneur in
generation t with her realized productivity, the knowledge available to generation t + 1 is
Tt+1 =
Z
i
Tt q(zti ) it .
This is essentially the same as assuming that productivity growth is increasing in the level of
productivity-enhancing investment (e.g., R&D), as usually done in endogenous-growth models.11
3
Cyclical composition of investment
In this section we analyze the effect of financial development on the level and the cyclical behavior
of the two types of investment. We first consider the benchmark case of complete financial markets;
we then contrast it with the case of tight credit constraints.
11
See for example Barro-Sala-i-Martin (1999) and Aghion-Howitt (1998).
7
3.1
Complete markets
When credit markets are perfect, entrepreneurs can always meet their liquidity shocks, ensuring
that long-term investment pays out with probability one. Expected wealth is thus
i
= at π(kti ) + Et at+1 q(zti ) + (1 + rt )bit ,
Et wt+1
which the entrepreneurs maximize with respect to (kti , zti , bit ) subject to the budget constraint (1).
Obviously, all entrepreneurs make identical choices and therefore we can drop the i superscripts.
Since π and q are both strictly concave, the following first-order conditions are both necessary and
sufficient for an optimal solution:
at π 0 (kt ) = 1 + rt
and
Et at+1 q 0 (zt ) = 1 + rt .
It follows that the marginal rate of substitution between the two types of investment in equilibrium
is given by
q 0 (zt )
at
=
= a1−ρ
,
t
0
π (kt )
Et at+1
(4)
which is increasing in at as long as ρ < 1.
In equilibrium, the (day) interest rate rt adjusts so that the excess demand for the bond is zero,
or equivalently that the resource constraint is satisfied:
kt + zt = w.
(5)
This essentially imposes that the supply of savings is acyclical.12 Combining (4) and (5) implies
that, in general equilibrium, an increase in at reduces zt , increases kt and increases rt .
Proposition 1 Under complete markets, the share of short-term investment is procyclical, whereas
the share of long-term investment is countercyclical.
Note that, by (5), the aggregate level of investment is acyclical, but its composition is not. As
long as there is mean-reversion in the business cycle, profits in the immediate future (i.e., the return
to short-term investment) is more sensitive to the contemporaneous state of the economy than the
present value of profits anticipated further in the future (i.e., the return to long-term investment).
In other words, the demand for both types of investment is procyclical, but the demand for longterm investment is less procyclical than the demand for short-term investment, which explains why
in equilibrium zt is countercyclical under complete markets.
12
That in equilibrium every entrepreneur holds no bonds follows from our assumption that all entrepreneurs are
ex ante identical and that the net supply of the bond is zero.
8
Example 1. Suppose that π(k) = kα and q(z) = z α , 0 < α < 1. Condition (4) then reduces
, which together with (5) implies
to (kt /zt )1−α = a1−ρ
t
kt =
aηt
w
1 + aηt
and
zt =
1
w,
1 + aηt
where η = (1 − ρ)/(1 − α) > 0. Hence, zt is countercyclical (i.e., decreasing in at ), whereas kt is
procyclical (i.e., increasing in at ).
3.2
Incomplete markets
Credit constraints limit entrepreneurs’ borrowing capacity to a finite multiple of their current wealth
in both periods of life. The entrepreneurs’ investment problem is thus given by
¡ £
¤¢
max {at π(kti ) + Et at+1 q(zti )F µ at π(kti ) + (1 + rt )bit + (1 + rt )bit }
kti ,zti ,bit
s.t.
kti + zti + bit ≤ w,
(6)
kti + zti ≤ µw
where F (µ[at π(kti ) + (1 + rt )bit ]) is simply the probability that the liquidity shock will be met
(equivalently, that long-term investment will pay out).
We assume that π, q, and F are such that the objective in (6) is strictly concave; the first-order
conditions are then both necessary and sufficient and all entrepreneurs make identical choices in
equilibrium (so that we can again drop the i subscripts). The assumption of no storage within
periods implies that the first constraint is never binding in equilibrium; by the resource constraint
(3), we indeed have kt + zt = w < µw. The first-order conditions with respect to kti and zti can then
be expressed as follows:
£
¤
at π 0 (kt ) + Et at+1 q(zt )f (µxt ) µ at π 0 (kt ) − (1 + rt ) = 1 + rt ,
Et at+1 q 0 (zt )F (µxt ) − Et at+1 q(zt )f (µxt ) µ (1 + rt ) = 1 + rt ,
where xt = at π(kt ) + (1 + rt )bt . The condition for kt is obviously satisfied at
at π 0 (kt ) = 1 + rt ,
(7)
which means that the demand for kt is not affected by credit constraints. The condition for zt , on
the other hand, reduces to
¸
1 + Et at+1 q(zt )f (µxt ) µ
m
Et at+1 q (zt ) = (1 + rt )
F (µxt )
0
∙
The demand for long-term investment is thus no more than under complete markets.
9
(8)
In equilibrium, the interest rate rt adjusts so that bt = 0 and therefore kt + zt = w and
xt = at π (kt ). Let µ̄ ≡ c̄/ (āπ(1)) . Note that µ ≤ µ̄ suffices for µxt < c̄ to hold for all at , in which
case F (µxt ) < 1, f (µxt ) > 0, and the term in brackets in (8) is strictly greater than one.
Proposition 2 Suppose µ ≤ µ̄. For any realization at , incomplete markets lead to a lower interest
rate rt , a higher short-term investment kt , and a lower long-term investment zt as compared to
complete markets.
Next consider the cyclical behavior of investment. Using F (µxt ) = (µat π (kt ) /c̄)φ along with
(7), (8), and Et at+1 = aρt , we infer that the equilibrium allocation of savings satisfies
q 0 (zt )
a1−ρ−φ
q(zt )
t
=
+φ
φ
π 0 (kt )
π(k
t)
[µπ (kt ) /c̄]
(9)
Together with the resource constraint, zt + kt = w, the above condition implies that zt is increasing
(decreasing) in at if 1 − ρ − φ < 0 (> 0).
Proposition 3 Suppose µ ≤ µ̄ and φ > 1−ρ. The share of long-term investment is now procyclical
and that of short-term investment is countercyclical.
The intuition for this result is simple. The opportunity-cost effect, which tends to make the
relative demand for long-term investment countercyclical, is equally present under complete and
incomplete markets. But a second effect emerges when µ ≤ µ̄, for then the probability that the
liquidity shock will not be met is less than one in all states and, most importantly, is higher in a
recession than in a boom. This liquidity-risk effect tends to make the relative demand for long-term
investment procyclical. The condition φ > 1 − ρ then ensures that this latter effect dominates: the
opportunity-cost effect is weaker the higher the persistence ρ in the business cycle, whereas the
liquidity-risk effect is stronger the higher the cyclical elasticity φ of the probability of meeting the
liquidity shock.
Finally, note that µ controls primarily the average level of liquidity risk, whereas φ controls its
cyclical elasticity. Although the two parameters are unrelated in our model, lower levels of financial
development may be typically associated with both a higher mean level and a higher cyclicality of
liquidity risk. Moreover, in our model, the cyclicality of liquidity risk is also affected by µ when
µ > µ̄, for then a higher µ implies a larger region of at for which the liquidity risk becomes zero and
therefore becomes locally insensitive to fluctuations in at . For these reasons, in the empirical part
of the paper we shall identify lower financial development in the data to a combination of lower µ
and higher φ in the model.
Example 2. Suppose π(k) = k α , q(z) = z α , α < 1, c̄ = 1, and 1 − ρ < φ < (1 − α) /α.13
13
The assumption φ < (1 − α) /α suffices for the objective in (6) to be strictly concave and therefore for the
first-order conditions to be sufficient.
10
level zt
0.5
elasticity
∂lnztê∂lnat
0.8
1
0.45
0.6
0.95
0.4
0.4
0.9
0.2
0.35
0.3
-0.2
2
3
4
5
µ
survival rate F@µatπHktLD
0.85
2
3
4
5
-0.4
µ
0.8
2
3
4
5
µ
Figure 2: The effect of credit constraints (µ) on the level, the cyclical elasticity, and the survival
rate of productivity-enhancing investment.
Condition (9) then reduces to
,
ψ (zt ) = µφ aφ+ρ−1
t
(10)
where ψ (z) = z 1−α (w − z)−φα (w − (1 + φ) z)−1 . Clearly, ψ (z) increases with z, whereas µφ aφ+ρ−1
increases with µ and a. (10) can thus be solved for zt as an increasing function of µ and at .
Example 3. Suppose the same technologies as in the above example, but now let the distribution of c be log-normal, in which case the elasticity φ becomes endogenous. Figure 2 illustrates the
impact of µ on the equilibrium level of long-term investment zt , its cyclical elasticity ∂ ln zt /∂ ln at ,
and its survival rate, δ (at ) = F (µat π (kt )) (all evaluated at at = 1). In this example, too, tighter
constraints lead to a lower average and more procyclical long-term investment.
4
Amplification, volatility and growth
In this section, we analyze how financial constraints affect aggregate volatility, mean growth, and
the relation between the two.
4.1
Complete markets
Under complete financial markets, productivity-enhancing investment is never interrupted. Hence,
letting z ∗ (at ) denote the complete-markets equilibrium level of long-term investment, the growth
rate of technology is
Tt+1
= γ ∗ (at ) ≡ q (z ∗ (at )) .
Tt
Since z ∗ (at ) is decreasing in at , γ ∗ (at ) is also decreasing in at .
Corollary 1 Under complete markets, the endogenous component of productivity growth is countercyclical and therefore mitigates the business cycle.
11
Consider next the causal effect of volatility on growth. Whether a higher variance in at results in
higher or lower mean growth ultimately depends upon the curvatures of q (·) and z (·) . In the CobbDouglas case of Example 1 in Section 3.1, it is easy to check that γ ∗ (·) is necessarily convex at least
in a neighborhood of the mean productivity shock. A small mean-preserving spread in at starting
from zero variance then necessarily increases the mean rate of technological growth. In general,
however, γ (·) may have both convex and concave segments and therefore the complete-markets
effect of volatility on growth is a priori ambiguous.
4.2
Incomplete markets
Since only those firms that can meet their adjustment costs are able to innovate and thereby
contribute to aggregate productivity growth, the growth rate of technology is now given by
Tt+1
= γ (at ) ≡ q (z(at )) δ(at )
Tt
where z(at ) is the incomplete-markets equilibrium level of long-term investment and δ(at ) ≡
F (µat π (w − z(at ))) is the equilibrium probability of covering the liquidity shock (by the law of
large numbers, this is also equal to the equilibrium fraction of entrepreneurs who successfully overcome their liquidity shocks). Clearly, µ ≤ µ̄ and φ > 1 − ρ suffice for δ (at ) < 1 and z (at ) < z ∗ (at )
to hold for all at , as well as for both δ (at ) and zt (at ) to be strictly increasing in at . It follows that
γ (at ) < γ ∗ (at ) for all at , and that γ (at ) is strictly increasing in at .
Corollary 2 Under sufficiently incomplete markets (i.e., for µ ≤ µ̄ and φ > 1−ρ), the endogenous
component of productivity growth is procyclical and therefore amplifies the business cycle. Moreover,
productivity growth is strictly less than that under complete markets in all states.
Note how the amplification result contrasts with the mitigating effect of long-term investment
under complete markets (Corollary 1). While the opportunity-cost effect implies that long-term
investment and therefore productivity growth are countercyclical under complete markets, the
liquidity-risk effect contributes to making productivity growth procyclical under incomplete markets via two channels: first, by imputing procyclicality in the demand for long-term investment;
and second, by making the success probability of long-term investments higher in booms than in
recessions.
Consider now the relationship between volatility and growth. For any given variance in at ,
a reduction in µ both increases the variance and reduces the mean of Tt+1 /Tt . The negative
cross-country correlation between growth volatility and mean growth observed in the data may
therefore reflect a spurious correlation imputed by cross-country differences in financial development. Moreover, this negative correlation need not diminish once one controls for the level of
aggregate investment; what matters is the composition of investment.
12
The causal effect of exogenous volatility on mean growth, on the other hand, depends again on
the curvatures of q (·) and z (·) , as well as that of δ (·) . As with complete markets, the curvature
of z (·) is ambiguous. Moreover, the curvature of δ (·) depends on the distribution of the liquidity
shock. The causal effect of a mean-preserving spread in at on the mean of Tt+1 /Tt thus remains
ambiguous in general. Nevertheless, the following examples provide some insight into the causal
effect of volatility under incomplete markets.
Example 4. Suppose that the adjustment cost c is 0 with probability p ∈ (0, 1) and c̄ > 0 with
probability 1 − p. Suppose further that z (at ) = ẑ ∈ (0, w) for all at , that is, ignore the cyclicality
in long-term investment. Normalizing π (w − ẑ) = q (ẑ) = 1, it follows that
γ(at ) = δ(at ) =
(
1
p
if µat ≥ c̄
if µat < c̄
Moreover, recall that the productivity shock at has unconditional mean 1 and support [a, ā].
When µ > c̄, firms face no liquidity risk in the absence of macroeconomic volatility (i.e., when
a = ā = 1) or, more generally, as long as volatility is small enough that a > c̄/µ. But as soon as
a < c̄/µ, a mean-preserving spread in at decreases mean growth by increasing the probability that
the economy will be in a (sufficiently severe) slump where a positive fraction of firms fail to meet
their liquidity shocks and complete their long-term investments.
When µ < c̄, on the other hand, only a fraction of firms succeed in completing their long-term
investments in the absence of volatility or, more generally, as long as ā < c̄/µ. But, as soon as
ā > c̄/µ, a mean-preserving spread in at now increases mean growth by increasing the probability
that the economy will enter a sufficiently good boom where all long-term investments are completed.
This example highlights an important reason why the causal effect of volatility on growth may
be non-monotonic under incomplete markets. When liquidity shocks and credit constraints are
sufficiently severe that the mean probability of success is very low, higher volatility may increase
mean growth by increase the chances for “resurrection”; otherwise higher volatility is likely to
decrease mean growth by increasing the chances for failure.
Example 5. Suppose that z (at ) = ẑ for all at , as in the previous example, but now let c be
uniform over [0, c̄]. Normalizing again π (w − ẑ) = q (ẑ) = 1, we now have
γ(at ) = δ(at ) = min {µat /c̄, 1} .
Whereas δ (·) was S-shaped (i.e., convex for low a, concave for high a) in the previous example, now
it is globally concave. In other words, the resurrection effect discussed above has now disappeared.
It follows that a sufficiently large mean-preserving spread in at necessarily reduces mean growth.
Furthermore, if µ > c̄, the negative effect of volatility on mean growth is higher the lower µ.
13
mean growth
volatility
7.6
1
7.4
0.8
7.2
0.6
7
6.8
0.4
6.6
0.2
6.4
0.1
0.2
0.3
0.4
0.5
0
0
σ
0.1
0.2
0.3
0.4
0.5
σ
Figure 3: The effect of uncertainty (σ) on growth and volatility; dashed lines for perfect markets,
solid lines for tight credit constraints.
Example 6. Consider the same specification as in Example 3 of Section 3.2, but now assume
that ln at follows a Gaussian AR (1) and let σ denote the standard deviation of the innovations
in at . Figure 3 illustrates how the mean and the standard deviation of the growth rate Tt+1 /Tt
vary with σ. The dashed lines represent complete credit markets (µ = ∞), whereas the solid ones
correspond to incomplete markets (µ < ∞).
For any level of σ, incomplete markets are associated with lower growth and higher volatility
than complete markets. Moreover, an increase in σ has a strong negative effect on mean growth
under incomplete markets. This is explained by two factors. First, the average liquidity risk is
relatively small, which ensures that the resurrection effect is weak. Second, as the innovation
probability δ(a) tends to be concave in a, the optimal level of long-term investment z (a) also
tends to be concave in a under sufficiently incomplete markets, whereas it is convex at least in a
neighborhood of the mean productivity level under complete markets; the concavity of z (a) then
implies that an increase in σ tends to reduce the mean level of z.
5
Empirical analysis
In this section we test the main predictions of the model in a panel of countries over the 1960-2000
period. We construct a measure of exogenous shocks using export-weighted changes in international
commodity prices, as described in detail below. We then ask whether a lower level of financial
development increases the responsiveness of growth to exogenous shocks (amplification effect) and
whether this effect is channeled through the rate or the composition of investment (amplification
channel). We also revisit the relation between volatility and growth in the cross-section of countries.
5.1
Data description
As a measure of financial development we use private credit, the value of credit extended to the
private sector by banks and other financial intermediaries as a share of GDP. This is a standard
14
indicator in the finance and growth literature and it comes from Levine, Loyaza and Beck (2000).
It is usually preferred to other measures of financial development because it excludes credit granted
to the public sector and funds provided from central or development banks.
We compute annual growth as the log difference of per capita income from the Penn World
Tables mark 6.1 (PWT). The measures of growth and volatility used in Tables 1 and 7 are the
country-specific means and standard deviations of annual growth over the 1960-1995 period.
To study the responsiveness of the economy to exogenous shocks, we construct the following
proxy. Using data on the international prices of 42 products between 1960 and 2000 from the International Financial Statistics Database of the IMF (IFS), we calculate the annual inflation/deflation
rate for each commodity. We then average the share of this commodity in a country’s exports
in 1985, 1986, and 1987 as reported in the World Trade Analyzer (WTA).14 Finally, we take a
weighted average of price changes across all commodities using the corresponding export shares as
weights. We thus obtain a country-by-year-specific measure, which we call commodity-price shocks.
To test whether there is any amplification effect of financial constraints, we examine the variation in the sensitivity of growth to commodity-price shocks across different levels of financial
development. To analyze the amplification channel, on the other hand, we also need data on the
composition of investment. The model makes predictions for the share of long-term productivityenhancing investment; we proxy this by the share of R&D in total investment. Unfortunately, data
availability limits our sample to 14 OECD countries between 1973-1999 for which the OECD reports spending on research and development in the ANBERD database. We combine this measure
with data on total investment as a share of GDP from the PWT.
When analyzing the reaction of the economy to shocks we also control for overall property
rights (property) and intellectual property rights (ipr). The former is a broad measure from various
editions of the Fraser Institute’s Economic Freedom of the World database, whereas the latter is
a narrower index constructed by Ginarte and Park (1997). For these variables we use the data as
compiled by Caselli and Wilson (2003).15
Finally, the demographics data comes from the PWT; the schooling data from Barro and Lee
(1997); and the various policy variables used in Tables 1 and 7 — the share of government in GDP,
inflation, the black market exchange rate premium, and openness to trade — from Levine et al.
(2000).
5.2
Amplification effect of credit constraints
We begin by examining the sensitivity of growth to shocks in a panel of 72 countries and 6 periods,
where a period consists of 5 consecutive non-overlapping years between 1960 and 1990. We estimate
14
These were the earliest years for which complete data were available at the country-commodity level.
Data on property rights and intellectual property rights is only available at 5-year intervals. We annualize the
data by imposing a constant growth rate within each 5-year period.
15
15
the following specification:
∆yit = α0 + α1 · yit + α2 · shockit + α3 · crediti_
+γ · crediti_ · shockit + β · Xit + µi + εit
(11)
where ∆yit is growth for country i in period t, yit is beginning-of-period per capita income (in logs),
and Xit is a vector of controls (namely, period-average population growth and secondary school
enrollment). To address the potential for omitted intransient country-level variables, we include
country fixed effects and cluster errors by country.
We consider two alternative measures for credit. In the first two columns of Table 2 we use
the average value of private credit over the contemporaneous 5-year interval. In accordance with
previous findings in the literature, we observe a negative coefficient on initial income (evidence of
convergence) and a strong positive overall effect of credit. As expected, the overall impact of shock
on ∆y is also positive, because an increase in shock represents an improvement in the exporting
opportunities available to a country.
We are more interested, however, in the interaction of credit and shock. In line with our
theoretical predictions, we find a negative coefficient, suggesting that financial development reduces
the sensitivity to exogenous shocks. While the coefficient is imprecisely estimated in column 1, it
becomes statistically significant when we add time fixed effects in the second column.
[insert Table 2 here]
One concern with using the contemporaneous value of credit is that it varies with the business
cycle and may thus capture the impact of some other cyclical omitted variable. Note, however,
that for the interaction term to be spurious a beneficial shock must be associated with both higher
growth rates and lower levels of private credit, which seems unlikely. Moreover, the estimate of
γ is robust to the introduction of either a quadratic term for shock or the interaction of y and
shock, which speaks further against such a bias (results not reported). Nevertheless, columns 3
and 4 repeat the estimation in the first two columns with the average value of private credit over
the entire 1960-1990 period, which is immune to the above omitted variable bias. The interaction
term is now highly significant with and without time fixed effects; it also increases in magnitude.
While the 1960-1990 average addresses potential bias concerns, it does not capture the significant time variation in the level of financial development; it may thus be a poorer proxy than the
contemporaneous value of credit. For that reason, in the remaining of the paper we estimate all
specifications with both time-variant and country-fixed measures of private credit.
Including time fixed effects, on the other hand, takes away the component in shock that is
common to all countries in a given period and therefore isolates the response to the idiosyncratic
component of shock. Although the empirical results suggest that the amplification effect of credit
16
constraints differs between world-wide and idiosyncratic shocks, our model does not make such a
distinction. To avoid taking a stance and explore the potentially differential effects of the two shock
components, we continue the empirical analysis both with and without time fixed effects.16
The above results avoided the lag structure of the response of growth to shocks by aggregating
over 5-year interval. We henceforth focus on an annual panel of 65 countries between 1960 and
2000 and extend the specification above as follows:
∆yit = α0 + δ 0 · shockit + δ −1 · shockit−1 + δ −2 · shockit−2 +
+γ 0 · crediti_ · shockit + γ −1 · crediti_ · shockit−1 + γ −2 · crediti_ · shockit−2 +
(12)
+αc · crediti_ + αy · yit−2 + µi + εit
Now ∆y is annual growth, y is per capita income lagged two years, and the three shock variables
correspond to the contemporaneous, 1-year lagged, and 2-year lagged commodity-price shocks. The
estimation of all lagged shock terms is possible because of the low autocorrelation in the commodity
price shocks.17 As before, we include country fixed effects and cluster errors at the country level.
Table 3 reports our results. The first column presents our baseline specification with a lagged
moving average of private credit over the five years immediately preceding time t. As the model
predicts, tighter credit results in higher sensitivity to shocks, especially at two lags. We obtain the
same result when we use the 1960-2000 average value of credit in column 2. Only the twice-lagged
shock is now precisely estimated, but this may be partly due to the multi-colinearity between the
three shock variables. For this reason, we also report F-statistics for the joint significance of all
three interaction terms, as well as that of only the 2 lagged shock interactions. In most cases, the
data favor the inclusion of the interaction terms.
[insert Table 3 here]
Columns 1 and 2 restrict the sample to countries in which the moving average of private credit
is always above 10% of GDP. This cut-off is motivated by the concern that variation in the measure
of credit within the 0-10% range is unlikely to be informative about the variation in the availability
of funds. Alternatively, financial development may not importantly affect the likelihood of meeting
a liquidity shock unless it is above a minimum threshold level. When we do not impose this cut-off
in columns 3 and 4 the interaction terms are less precisely estimated and lose joint significance.
In contrast, the results are generally robust to higher cut-off values, such as 15%, 20%, or 25%;
16
A proxy for shocks more commonly used in the growth literature is changes in the terms of trade. We prefer to
use commodity-price shocks because the time variation in exchange rates that enters the terms of trade calculation
is largely endogenous to the business cycle. In contrast, the time variation in the price of each commodity is largely
exogenous to a country, and the weights we use to aggregate across commodities vary in the cross-section but not
over time. When we use data on terms of trade shocks from Barro and Lee (1997), the interaction terms are of the
correct sign but imprecisely estimated.
17
We calculated the correlation coefficient between shockt and shockt−1 for each country. In our sample of 65
countries the average autocorrelation coefficient was 0.08 with a standard deviation of 0.16.
17
Columns 5 and 6 repeat the first two columns for the 20% cut-off. In light of these results, we use
the 10% cut-off throughout the rest of the analysis.18
In columns 7 and 8 we check the robustness of our results to alternative measures of private
credit: a lagged moving average over the (t − 6, t − 10) period; and the initial value of credit,
computed for each country as the average of the first 5 years for which credit data are available.
Both measures predate the commodity-price shocks and the length of a business cycle. Columns
9 and 10, on the other hand, add year fixed effects and thus isolate the response to idiosyncratic
shocks. The results are largely unchanged, although the significance of the interaction terms varies
across specifications.
We next address another omitted-variable concern: the possibility that our estimates capture
the interaction effect of some other institutional variable. For example, if property rights are
positively correlated with credit availability and growth reacts less to adverse shocks in countries
with better property rights, the interaction terms reported in Table 3 may reflect the mitigating
effect of property rights rather than that of financial development.
[insert Table 4 here]
Table 4 thus revisits the baseline specifications of Table 3 after controlling for other institutional
variables. For comparison, column 1 reproduces the first column of Table 3. Column 2 adds the
interactions of shock with ipr and property. Column 3 instead includes the interactions of shock
with initial income y, a proxy for the overall level of economic development. Column 4 combines
all control interactions and column 5 adds time fixed effects. Columns 6 and 7 then repeat 4 and 5
using the 1960-2000 average level of credit rather than the (t − 5, t − 1) average. In all specifications
the private-credit interaction terms remain significant.19,20
5.3
Amplification channel
The evidence presented so far supports the prediction that tighter credit amplifies the business
cycle, but it does not identify the transmission channel as being the composition of investment or
any other channel. In this subsection, we examine how credit affects the sensitivity of both the
level and composition of investment to shocks.21
18
When we estimate the same specification in the remaining sample of countries, which fall below the 10% cut-off,
we observe highly insignificant coefficients, although usually of the same sign. We do not really know why this group
of countries behaves differently; our guess is that there is simply too much noise in this group.
19
Our results also survive the inclusion of the interaction of shock with the size of government and the black market
premium (results not reported).
20
In unreported regressions we have explored the possibility that financial development affects also the persistence
of fluctuations. The interaction of private credit with y enters positively, suggesting that persistence is higher in more
financially developed countries. This effect however is not statistically significant.
21
Walde and Woitek (2004) find that the level of R&D expenditure tends to be procyclical in the G7 countries
between 1973 and 2000. (See also Walde, 2004.) In contrast, we focus on the cyclical variation of R&D as a share of
total investment.
18
Using annual data on 14 OECD countries between 1973 and 1999 we estimate the following two
regressions:
R&D/Iit = α0 + δ 0 · shockit + δ −1 · shockit−1 + δ −2 · shockit−2 +
+γ 0 · crediti_ · shockit + γ −1 · crediti_ · shockit−1 + γ −2 · crediti_ · shockit−2 +
+αc · crediti_ + αy · yit−2 + µi + εit
I/Yit
(13)
= α0 + δ 0 · shockit + δ −1 · shockit−1 + δ −2 · shockit−2 +
+γ 0 · crediti_ · shockit + γ −1 · crediti_ · shockit−1 + γ −2 · crediti_ · shockit−2 +
+αc · crediti_ + αy · yit−2 + µi + εit
(14)
The dependent variables here are R&D as a share of total investment — our proxy for long-term
growth-enhancing investment — and total investment as a fraction of GDP for country i in year t.
As before, we consider contemporaneous, 1-year lagged, and 2-year lagged commodity-price shocks,
include country fixed effects, and cluster errors by country. Note that in the sample of countries
with R&D data we never observe values of private credit below 10%.
[insert Table 5 here]
The results from estimating (13) are reported in Table 5. Columns 1-3 use the moving average
of private credit over the immediately preceding five years, whereas columns 4-6 use the 1973-1999
average for each country. Columns 2 and 5 control for the interactions of shocks with ipr, property,
and per capita income lagged 2 years. Finally, year fixed effects are added in columns 3 and 6.
Across all specifications, the direct effect of shocks (δ) is typically positive, whereas the interaction of shocks with credit (γ) is negative. Moreover, the total effect (δ + γ · credit) is typically
positive for countries with the lowest values of credit and negative for the ones with the highest
credit. In particular, we estimate statistically and economically significant negative coefficients on
the interaction with once- and twice-lagged shocks when we include time effects. Once again, this
may suggest that the amplification channel we are emphasizing in the model and in this regression
is more likely to capture the response of countries to idiosyncratic exogenous shocks rather than to
common shocks.
[insert Table 6 here]
In sharp contrast with the above findings, when we turn to the results for (14) in Table 6, we
find no evidence that tighter credit increases the sensitivity of I/Y to shocks. If anything, the
reverse is true: most interaction terms enter with a positive sign.
These findings are far from conclusive, since they are limited to a sample of OECD countries,
a specific type of shocks and a specific decomposition of investment. Nevertheless, they appear to
19
reject the standard amplification channel involving the level of aggregate investment, and instead
point to a composition effect as in our model.
5.4
Revisiting the impact of volatility on growth
As discussed in the introduction, the negative relation between volatility and growth observed in
the cross-section of countries need not reflect causality. Moreover, the causal effect of volatility
is ambiguous in general. An interesting possibility, however, was raised by examples 5 and 6 in
Section 4: to the extent that liquidity risk increases with aggregate volatility, volatility can have a
detrimental effect on growth, and the more so the tighter the credit constraints.
[insert Table 7 here]
We examine this possibility in Table 7. In column 1 we repeat the Ramey-Ramey regression
with the addition of private credit and its interaction with volatility.22 Consistent with the insight
above, the negative impact of volatility on growth tends to be stronger in countries with lower
financial development. This effect is economically important: in column 1, for example, a onestandard-deviation increase in the average level of financial development reduces the impact of a
1% rise in volatility by −0.68% (= 0.018 · 38).
The interaction effect is robust to the inclusion of demographics, property rights and policy
controls, and independent of the overall level of investment, as columns 2-4 show. It may be biased,
however, because of the endogeneity of volatility. For that reason, columns 5-8 repeat the regressions
instrumenting volatility with the standard deviation of commodity-price shocks. The interaction
term now remains of the right sign and comparable magnitude but loses statistical significance,
which may be due to the fact that commodity-price shocks explain only a small fraction of total
volatility.
Further research is therefore necessary before a solid causal interpretation of the above finding
can be established.23 Nevertheless, at first pass the data appear to suggest that the potentially
detrimental causal effect of volatility on growth is larger in countries with lower financial development, which may have important implications for welfare and policy.
6
Concluding remarks
This paper investigated how financial development affects the cyclical composition of investment
and the implications this has for volatility and growth. We first considered a simple model that
22
For consistency we present results for the countries that meet the 10% cut-off. The results are very similar in
the sample of 72 countries from Table 1.
23
Supportive is also the historical evidence in Blattman, Hwang and Williamson (2004). Using panel data for 35
countries over the 1870-1939 period, they find that volatility as measured by term of trade shocks is harmful for
growth in the Periphery, but not in the Core.
20
endogenizes productivity-enhancing investment over the business cycle. We found that credit constraints make the fraction of productivity-enhancing investment more procyclical, thus amplifying
the variation in productivity and output even if they fail to amplify the variation in aggregate
savings. We then confronted these predictions with a cross-country panel and found evidence that
tighter financial constraints make R&D investment and growth more sensitive to shocks, while also
generating a more negative correlation between volatility and growth.
The model used in this paper was highly stylized. We nevertheless expect the main insights to
extend to more general frameworks as long as the key propagation channel — the effect of liquidity
risk on long-term productivity-enhancing investments — is preserved. An interesting direction for
future research would be to embed this mechanism into a full-fledged RBC model and examine in
detail the implications for the economy’s impulse responses to exogenous productivity and demand
shocks.24
Another fruitful direction for future research is the interplay between macroeconomic policy
and productivity growth.25 Extending the insights of this paper regarding the causal effect of
volatility on growth, Aghion, Barro and Marinescu (work in progress) investigate whether countercyclical budgetary policies have a stronger positive effect on long-run growth in less financially
developed countries. Aghion, Bacchetta, Ranciere, and Rogoff (2005), on the other hand, examine
the relationship between financial development, the choice of exchange-rate regime, and growth
performance.
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25
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21
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22
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[19] Gali, Jordi, and Mohamad Hammour (1991), “Long-run Effects of Business Cycles,” Columbia
University mimeo.
[20] Gavin, M., and Ricardo Hausmann (1996), “Make or Buy: Approaches to Financial Market
Integration,” Inter-American Development Bank Working Paper 337.
[21] Ginarte, Juan, and Walter Park (1997), “Determinants of Patent Rights: a Cross-National
Study,” Research Policy 26(3), 283-301.
[22] Hall, Robert (1991), “Recessions as Reorganization,” NBER Macroeconomics Annual 1991.
[23] Heston, Alan, Robert Summers, and Bettina Aten (2002), “Penn World Tables Version 6.1,”
Center for International Comparisons at the University of Pennsylvania.
[24] Holmstrom, Bengt, and Jean Tirole (1998), “Private and Public Supply of Liquidity,” Journal
of Political Economy 106, 1-40.
[25] Jones, Larry, Rodolfo Manuelli, and Ennio Stacchetti (2000), “Technology and Policy Shocks
in Models of Endogenous Growth,” Federal Reserve Bank of Minneapolis Working Paper 281.
[26] Kaldor, Nicholas (1954), “The Relation of Economic Growth and Cyclical Fluctuations,” Economic Journal 64, 53-71.
[27] King, Robert, and Ross Levine (1993), “Finance and Growth: Schumpeter Might Be Right,”
Quarterly Journal of Economics 108, 717-737.
[28] King, Robert, and Sergio Rebelo (1993), “Transitional Dynamics and Economic Growth in the
Neoclassical Model,” American Economic Review 83, 908-31.
[29] Kiyotaki, Nobuhiro, and John Moore (1997), “Credit Cycles,” Journal of Political Economy
105, 211-48.
[30] Koren, Miklos, and Silvana Tenreyro (2004), “Diversification and Development,” Harvard University and Federal Reserve Bank of Boston mimeo.
[31] Levine, Ross (1997), “Financial Development and Economic Growth: Views and Agenda,”
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[32] Levine, Ross, Thorsten Beck, and Asli Demirguc-Kunt (2001), “A New Database on the Structure and Development of the Financial Sector,” World Bank Economic Review.
23
[33] Levine, Ross, Norman Loyaza, and Thorsten Beck (2000), “Financial Intermediation and
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[38] Walde, Klaus (2004), “Endogenous Growth Cycles,” International Economic Review, forthcoming.
[39] Walde, Klaus, and Ulrich Woitek (2004), “R&D Expenditure in G7 Countries and the Implications for Endogenous Fluctuations and Growth,” Economics Letters 82, 91-97.
24
Table 1. Average growth, growth volatility and investment volatility
Dependent variable:
initial income
growth volatility
Average growth, 1960-1995
(1)
-0.0019
(-0.69)
-0.2796
(-2.63)***
(2)
-0.0175
(-5.66)***
-0.2641
(-2.78)***
investment/GDP
(3)
-0.0094
(-3.89)***
-0.1829
(-2.14)**
0.1742
(6.47)***
(4)
-0.0163
(-5.98)***
-0.2208
(-2.63)**
0.0963
(3.96)***
private credit
Growth volatility, 1960- Investment volatility,
1995
1960-1995
(5)
(6)
(7)
(8)
-0.0063
-0.0056
-0.0052
-0.0061
(-1.87)*
(-1.38)
(-1.11)
(-1.01)
-0.00024
(-2.45)**
-0.00012
(-0.90)
0.00003
(0.25)
0.00019
(1.11)
Controls:
pop growth, sec enroll
Levine et al. policy set
property rights
no
no
no
yes
yes
yes
no
no
no
yes
yes
yes
no
no
no
yes
yes
yes
no
no
no
yes
yes
yes
R-squared
N
0.0969
70
0.6018
59
0.4472
70
0.7013
59
0.2673
70
0.3755
59
0.0356
70
0.2856
59
Note: All regressors are averages over the 1960-1995 period, except for intellectual and property rights which are for 1970-1995 and 1970-1990
respectively. Initial income and secondary school enrollment are taken for 1960. Growth and investment volatility are constructed as the standard
deviation of annual growth and the share of total investment in GDP in the 1960-1995 period respectively. The Levine et al. policy set of controls
includes government size as a share of GDP, inflation, black market premium, and trade openness. Constant term not shown. t-statistics in
parenthesis. ***,**,* significant at 1%, 5%, and 10%.
Table 2. The response of growth to commodity price shocks: 5-year avgs
Dependent variable: 5-year avg. growth
private creditt
(1)
(2)
-0.0701
-0.0710
(-6.60)*** (-6.00)***
0.1243
0.1214
(2.20)**
(2.09)**
0.0387
0.0385
(2.97)*** (2.75)***
-0.2119
-0.2722
(-1.44)
(-1.78)*
1960-1990 avg
(3)
(4)
-0.0481
-0.0467
(-4.68)*** (-3.74)***
0.1686
0.1518
(2.79)*** (2.36)**
-0.4033
(-2.24)**
-0.4103
(-2.12)**
Controls:
pop growth, sec enroll
country fixed effects
period fixed effects
yes
yes
no
yes
yes
yes
yes
yes
no
yes
yes
yes
R-squared
# countries (groups)
N
0.5355
72
388
0.5521
72
388
0.4788
72
418
0.4925
72
418
Private credit measure:
initial income
shock
private credit
private credit*shock
Note: Commodity price shocks are export-weighted changes in the price of 42 commodities. All variables except for
private credit are averaged over 5-year non-overlapping periods from 1960 to 1990. Initial income is beginning of
period income. Private credit is averaged over the concurrent 5-year period in Columns (1) and (2) and over the 19601990 period in Columns (3) and (4). All regressions include a constant term, and cluster errors at the country level. tstatistics in parenthesis. ***,**,* significant at 1%, 5% and 10%.
Table 3. The response of growth to commodity price shocks
Dependent variable: annual growth
Baseline specifications
Private credit average:
(t-5,t-1)
1960-2000
(1)
(2)
shock t
-0.0130
0.0110
(-0.53)
(0.48)
shock t-1
-0.0154
0.0019
(-0.48)
(0.05)
shock t-2
0.0687
0.0651
(2.88)***
(2.39)**
priv credit
No threshold
(t-5,t-1)
1960-2000
(3)
(4)
-0.0050
-0.0215
(-0.24)
(-0.96)
0.0208
-0.0025
(1.01)
(-0.08)
0.0487
0.0283
(2.79)***
(1.17)
20% threshold
(t-5,t-1)
1960-2000
(5)
(6)
-0.0600
-0.0272
(-0.59)
(-0.30)
0.0133
0.0835
(0.09)
(0.92)
-0.0028
0.0951
(-0.03)
(1.34)
0.0849
(1.13)
-0.0240
(-0.23)
-0.0556
(-0.65)
0.0121
(1.48)
0.1412
(0.94)
-0.0533
(-0.25)
-0.0886
(-0.59)
0.0174
(2.69)***
0.0337
(0.47)
-0.0177
(-0.27)
-0.2083
(-3.05)***
-0.0302
(-0.46)
-0.0863
(-0.89)
-0.1980
(-2.43)**
0.0246
(2.92)***
0.0205
(0.32)
-0.0690
(-0.99)
-0.1044
(-1.72)*
Controls:
income t-2
country fixed effects
year fixed effects
yes
yes
no
yes
yes
no
yes
yes
no
yes
yes
no
F-tests:
all interaction terms
lagged interaction terms
0.0164
0.0074
0.1262
0.0587
0.3955
0.2294
R-squared
# countries
N
0.1739
65
1,923
0.1368
65
2,364
0.1162
109
3,104
priv credit*shock t
priv credit*shock t-1
priv credit*shock t-2
Other credit measures
(to,to+4)
(t-10,t-6)
(7)
(8)
-0.0117
-0.0006
(-0.59)
(-0.02)
-0.0256
-0.0402
(-0.67)
(-1.23)
0.0575
0.0498
(2.06)**
(1.85)*
0.0743
(0.51)
-0.1822
(-1.21)
-0.2423
(-1.80)*
0.0177
(2.05)**
0.0274
(0.47)
0.0273
(0.29)
-0.1752
(-1.96)*
yes
yes
no
yes
yes
no
0.6214
0.8004
0.5585
0.8351
0.0966
111
3,980
0.1935
29
829
Year fixed effects
(t-5,t-1)
1960-2000
(9)
(10)
-0.0307
-0.0022
(-1.33)
(-0.10)
0.0062
-0.0011
(0.19)
(-0.03)
0.0584
0.0602
(2.58)**
(2.18)**
0.0091
(0.10)
0.0748
(0.74)
-0.1936
(-1.90)*
0.0109
(1.63)
0.0193
(0.28)
0.0014
(0.02)
-0.1383
(-2.04)**
-0.0757
(-1.08)
0.0071
(0.08)
-0.1819
(-1.94)*
yes
yes
no
yes
yes
no
yes
yes
yes
yes
yes
yes
0.1476
0.1219
0.2108
0.1099
0.1085
0.0758
0.1388
0.0770
0.1828
0.1079
0.1215
29
1,044
0.1724
63
1,639
0.1359
65
2,364
0.2317
65
1,923
0.1828
65
2,364
Note: Annual 1960-2000 data, except where lost due to lags. shock t , shock t-1 , shock t-2 refer to the contemporaneous, 1-year and 2-year lagged commodity price shock. All regressions
include a constant term, and cluster errors at the country level. The sample is limited to countries whose (t-5, t-1) credit average is always above 10% of GDP (20% in Columns (5) and
(6)), except for Columns (3) and (4) where no threshold is imposed. t-statistics in parenthesis. ***,**,* significant at 1%, 5%, and 10%.
Table 4. The response of growth to commodity price shocks: robustness
Dependent variable: annual growth
Private credit and property
terms average:
(t-5,t-1) avg
1960-2000 avg
(7)
(1)
-0.0130
(-0.53)
-0.0154
(-0.48)
0.0687
(2.88)***
(2)
-0.1350
(-2.57)**
-0.0069
(-0.09)
0.0038
(0.06)
(3)
-0.0073
(-0.05)
-0.0102
(-0.07)
0.2319
(1.28)
(4)
-0.2405
(-1.39)
0.1682
(0.57)
0.0682
(0.30)
(5)
-0.2176
(-1.16)
0.0781
(0.25)
-0.0597
(-0.29)
(6)
0.0973
(0.76)
-0.0527
(-0.30)
0.0846
(0.59)
-0.0149
(-0.14)
-0.0298
(-0.18)
0.0381
(0.28)
0.0174
(2.69)***
0.0337
(0.47)
-0.0177
(-0.27)
-0.2083
(-3.05)***
0.0234
(2.41)**
0.0685
(0.67)
-0.0086
(-0.08)
-0.2544
(-2.26)**
0.0172
(2.63)**
0.0394
(0.54)
-0.0127
(-0.14)
-0.1514
(-1.82)*
0.0235
(2.42)**
0.0403
(0.37)
0.0388
(0.26)
-0.2382
(-1.94)*
0.0180
(1.78)*
0.0815
(0.71)
0.0150
(0.11)
-0.2240
(-1.90)*
-0.0830
(-0.76)
-0.1516
(-1.16)
-0.2563
(-2.59)**
-0.0740
(-0.70)
-0.0896
(-0.70)
-0.2515
(-2.52)**
Controls:
income t-2
country fixed effects
property rights and interactions
income interactions
year fixed effects
yes
yes
no
no
no
yes
yes
yes
no
no
yes
yes
no
yes
no
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
F-tests:
all credit interaction terms
lagged credit interaction terms
0.0164
0.0074
0.0636
0.0359
0.2950
0.1992
0.1972
0.1063
0.2116
0.1390
0.0765
0.0375
0.0847
0.0490
R-squared
# countries
N
0.1739
65
1,923
0.2383
53
1,044
0.1745
65
1,923
0.2397
53
1,044
0.2993
53
1,044
0.1513
57
2,109
0.1979
57
2,109
shock t
shock t-1
shock t-2
priv credit
priv credit*shock t
priv credit*shock t-1
priv credit*shock t-2
Note: Annual 1960-2000 data, except where lost due to lags. shock t , shock t-1 , shock t-2 refer to the contemporaneous, 1-year and 2-year
lagged commodity price shock. All regressions include a constant term, and cluster errors at the country level. The sample is limited to countries
whose (t-5, t-1) credit average is always above 10% of GDP. Private credit and the property rights terms are averaged as indicated in the
column heading. Income interactions use twice lagged per capita income in Columns (1)-(5) and the 1960-2000 average in Columns (6)-(7). tstatistics in parenthesis. ***,**,* significant at 1%, 5%, and 10%.
Table 5. The response of R&D to commodity price shocks
Dependent variable: R&D/investment
Private credit and property
terms average:
(t-5,t-1) avg
1973-1999 avg
(1)
(2)
(3)
(4)
(5)
(6)
shock t
0.0903
(0.34)
1.2825
(0.38)
3.5864
(0.87)
0.2175
(0.54)
2.5729
(0.18)
5.0466
(0.33)
shock t-1
-0.1156
(-0.51)
5.7272
(2.74)**
3.0835
(0.86)
0.3550
(0.97)
12.3290
(1.13)
24.5633
(2.15)*
shock t-2
0.3867
(1.29)
7.2179
(1.66)
1.6775
(0.42)
0.1002
(0.23)
7.1779
(0.78)
13.4177
(0.96)
priv credit
0.0654
(0.31)
0.0365
(0.24)
0.0252
(0.16)
priv credit*shock t
-0.2010
(-0.51)
-0.0233
(-0.07)
-0.0887
(-0.34)
-0.3391
(-0.63)
-0.5424
(-0.57)
-0.7318
(-0.84)
priv credit*shock t-1
0.0692
(0.15)
-0.8823
(-1.56)
0.1214
(0.40)
-0.9269
(-1.45)
0.0663
(0.19)
-1.0567
(-1.90)*
-0.5605
(-0.90)
-0.3538
(-0.52)
-0.8399
(-0.94)
-0.9251
(-0.93)
-1.2364
(-2.05)*
-2.2883
(-2.16)**
Controls:
income t-2
country fixed effects
property rights and interactions
income interactions
year fixed effects
yes
yes
no
no
no
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
yes
yes
no
no
no
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
F-tests:
all credit interaction terms
lagged credit interaction terms
0.4076
0.2497
0.1677
0.3181
0.3193
0.2023
0.6837
0.4878
0.7677
0.6396
0.0860
0.1296
R-squared
# countries
N
0.8396
14
342
0.9205
14
307
0.9303
14
307
0.8482
14
357
0.8534
14
357
0.8909
14
357
priv credit*shock t-2
Note: Annual 1973-1999 data, except where lost due to lags. shock t , shock t-1 , shock t-2 refer to the contemporaneous, 1-year and
2-year lagged commodity price shock. All regressions include a constant term, and cluster errors at the country level. Private credit
and the property rights terms are averaged as indicated in the column heading. Income interactions use twice lagged per capita
income in Columns (1)-(3) and the 1973-1999 average in Columns (4)-(6). t-statistics in parenthesis. ***,**,* significant at 1%, 5%,
and 10%.
Table 6. The response of investment to commodity price shocks
Dependent variable: Investment/GDP
Private credit and property
terms average:
(t-5,t-1) avg
1973-1999 avg
(1)
(2)
(3)
(4)
(5)
(6)
shock t
-0.0165
(-0.21)
1.7262
(1.24)
0.8266
(0.46)
-0.2091
(-1.94)*
1.6659
(0.40)
-0.3910
(-0.09)
shock t-1
0.1178
(2.03)*
4.2493
(1.79)*
6.0103
(3.17)***
0.1239
(0.88)
5.2885
(1.27)
3.4971
(0.84)
shock t-2
-0.0285
(-0.32)
6.6502
(2.75)**
9.8319
(4.02)***
0.0079
(0.07)
7.8179
(1.42)
8.1249
(1.57)
priv credit
0.0148
(0.57)
0.0082
(0.33)
0.0035
(0.12)
priv credit*shock t
0.0950
(1.01)
0.1246
(1.17)
0.0257
(0.22)
0.3392
(2.78)**
0.3828
(1.78)*
0.1699
(0.77)
priv credit*shock t-1
-0.0579
(-0.55)
0.1968
(1.25)
0.1958
(1.84)*
0.4639
(2.78)**
0.1349
(1.52)
0.3315
(1.87)*
-0.0327
(-0.17)
0.1164
(0.79)
-0.1048
(-0.48)
0.4120
(2.88)**
0.0270
(0.15)
0.5205
(2.66)**
Controls:
income t-2
country fixed effects
property rights and interactions
income interactions
year fixed effects
yes
yes
no
no
no
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
yes
yes
no
no
no
yes
yes
yes
yes
no
yes
yes
yes
yes
yes
F-tests:
all credit interaction terms
lagged credit interaction terms
0.1428
0.1408
0.0375
0.0479
0.2233
0.1325
0.0962
0.6900
0.0003
0.0001
0.1130
0.0571
R-squared
# countries
N
0.7331
14
341
0.7952
14
307
0.8311
14
307
0.7224
14
356
0.7355
14
356
0.7756
14
356
priv credit*shock t-2
Note: Annual 1973-1999 data, except where lost due to lags. Sample limited to coutry-year observations with R&D data, as in Table
5. shock t , shock t-1 , shock t-2 refer to the contemporaneous, 1-year and 2-year lagged commodity price shock. All regressions
include a constant term, and cluster errors at the country level. Private credit and the property rights terms are averaged as
indicated in the column heading. Income interactions use twice lagged per capita income in Columns (1)-(3) and the 1973-1999
average in Columns (4)-(6). t-statistics in parenthesis. ***,**,* significant at 1%, 5%, and 10%.
Table 7. Growth, volatility and credit constraints
Dependent variable: avg. growth, 1960-1995
OLS
No investment
(1)
(2)
-0.0090
-0.0182
(-2.85)*** (-5.02)***
-0.8763
-0.7260
(-3.54)*** (2.80)***
-0.00037 0.00023
(-1.80)*
(0.57)
0.0184
0.0129
(3.19)***
(2.30)**
With investment
(3)
(4)
-0.0109
-0.0160
(-4.04)*** (-4.93)***
-0.6941
-0.5772
(-3.27)*** (-2.51)**
-0.00034
0.00007
(-1.94)*
(0.19)
0.0134
0.0097
(2.68)**
(1.95)*
0.1356
0.0964
(4.28)***
(3.16)***
IV: commodity price shocks volatility
No investment
With investment
(5)
(6)
(7)
(8)
-0.0276 -0.0371
-0.0267 -0.0120
(-0.90)
(-0.44)
(-0.68)
(-0.39)
-6.0000 -5.8680
-5.7602 -0.0495
(-0.63)
(-0.26)
(-0.40)
(-0.01)
-0.0032 -0.0023
-0.0031 -0.0001
(-0.46)
(-0.24)
(-0.35)
(-0.07)
0.0939
0.0725
0.0910 0.0062
(0.46)
(0.26)
(0.35)
(0.09)
0.0174 0.1286
(0.04)
(0.53)
Controls:
pop growth, sec enroll
Levine et al. policy set
property rights
private credit 2
no
no
no
no
yes
yes
yes
yes
no
no
no
no
yes
yes
yes
yes
no
no
no
no
yes
yes
yes
no
no
no
no
no
yes
yes
yes
no
F-test (volatility terms)
F-test (credit terms)
R-squared
N
0.0039
0.0005
0.3721
47
0.0303
0.0052
0.7467
42
0.0087
0.0218
0.5659
47
0.0526
0.0506
0.8151
42
0.7651
0.8982
0.9028
0.9403
0.9028
0.9403
0.8654
0.8776
47
42
47
42
initial income
growth volatility
private credit
volatility*private credit
investment/GDP
Note: All regressors are averages over the 1960-1995 period, except for intellectual and property rights which are for 1970-1995 and 19701990 respectively. Initial income and secondary school enrollment are taken for 1960. Growth and commodity price shocks volatility are
constructed as the standard deviation of annual growth and commodity price shocks in the 1960-1995 period respectively. The sample is
limited to the same set of countries as in Tables 3 and 4 (countries whose (t-5, t-1) credit average is always above 10% of GDP). The Levine
et al. policy set of controls includes government size as a share of GDP, inflation, black market premium, and trade openness. Constant term
not shown. t-statistics in parenthesis. ***,**,* significant at 1%, 5%, and 10%.
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