v import them. As a share of income, these exports
and imports are v-x and x-v, respectively. This kind of trade is usually referred to as
interindustry trade or factor-proportions trade. As a result, the model captures in a
stylized manner three broad empirical regularities regarding the patterns of trade: (a)
8 n particular, iv * X = . 1 JfJ(-o)e *dl=(p, 8) * dG(iT - rl) .
I1-cv c 00
To derive Equation (8), we equate the ratio of spending in both industries to the ratio of worldwide
production of both industries and then use Equations (3)-(7) to solve for p.
12
a large volume of intraindustry trade among rich countries, (b) substantial inter-
industry trade between rich and poor countries, and (c) little trade among poor
countries.
13
2. The Cross-section of Business Cycles
In the world economy described in the previous section, countries are subject
to two kinds of shocks. On the one hand, domestic productivity shocks shift industry
supplies. On the other hand, foreign productivity shocks shift industry demands. In
the presence of the competition bias or the cyclical bias, these shocks have different
effects in high-tech and low-tech industries. As a result, the aggregate response to
similar shocks differs across economies with different industrial structures. In other
words, the properties of the business cycles that countries experience depend on
the determinants of their industral structure, that is, on their factor endowments and
technology.
Domestic and Foreign Shocks as a Source of Business Cycles
The (demeaned) growth rate of income in a (gi,8,n)-country can be written as
a linear combination of domestic and foreign shocks: 9
dIny - E[dIny] = 4r * dn + dtr * dll (10)
The functions Q,(18,it) and t.(18,7r) measure the sensitivity of a country's
growth rate to domestic and foreign shocks, and are given by:
in = ( + A) - [X - a 1B + (1 - x) eco (1 1 )
=(1+x) -X a +(x-V) (Ea-e)P (12)
9To see this, apply Ito's lemma to the definition of income and use the expressions for equilibrium
factor prices and supplies in Equations (3)-(9).
14
Equations (1 0)-(1 2) provide a complete characterization of the business cycles
experienced by a (t,S,r)-country. Moreover, they show how business cycles differ
across countries, since the sensitivity of growth rates to domestic and foreign shocks
depends on the share in production of high-tech products, x. Finally, we note the
detrended growth rate of world average income, Y, is given by
dinY -E[dInY] = or - drl (13)
where the sensitivity of the world growth rate to innovations in the global component
of productivity is given by:
xn = (1+X).(v*a +(1-v) P) (14)
Let V(g,8,n) denote the standard deviation of the growth rate of a ( g,8,7r)-
country, and let C(p.,8,nr) denote the correlation of its growth rate with wodd average
income growth. These are the theoretical analogs to the Volatility and Comovement
graphs in Figure 1. Using Equations (1 0)-(1 4) and the properties of the shocks, we
defive the following result: 10
' The proof is simple, since we have closed-form solutions for both the volatility and comovement
statistics: V=4 (1-o).2+a (41 + H)2 andC= ( ) . Since 4,+4.
1(1 - a), 2 + a. (4 + ir)2
does not depend on x, V (C) will be downward (upward) sloping if and only if E, is decreasing in x. The
proposition describes the sign of 7c for different parameter values.
15
PROPOSITION 1: The functions C and V depend, at most, on x. Moreover:
(i) If E{= = =a *O- then TX = ax = 0 for all x;
(ii) If E >Ea 00 x then a <0 and -C >0 for all x; and
0 + ), ax ~ ax
(iii) If sp < a * then V>0 -and C< oforallx.
O+X ax ax
This is the first of a series of results that relate a country's industrial structure,
as measured by x, to the properties of its business cycles. Proposition 1 says that
the theoretical Volatility and Comovement graphs have the same slopes as their
empirical counterparts if the competition bias (low 0) and/or the cyclical bias (e5>Qa)
are strong enough. Equations (11)-(1 2) show that this same parameter restriction
implies that rich countries are less sensitive to domestic shocks (i.e. ,, is decreasing
with x), but more sensitive to foreign shocks (i.e. ; is increasing with x). In the
remainder of this section we provide intuition for this result.
Why Are Rich Countries Less Sensitive To Domestic Shocks?
Domestic shocks shift industry supplies. When these shocks are positive,
they raise production, wages and employment in both industries. When negative,
they lower production, wages and employment. However, to the extent that the
competition bias and the cyclical bias are important, these effects are larger in the 1-
industry than the a-industry.
It is useful to start with a benchmark case in which 0-oo and so that
neither the competition bias nor the cyclical bias are present. A favourable
productivity shock results in an increase in productivity of magnitude C*dn in both
industries, and has two familiar effects. Holding constant employment, increased
productivity directly raises production and hence income. This is nothing but the
16
celebrated Solow residual and consists of the sum of the growth rates of productivity
of both sectors, weighted by their shares in production, i.e. E.dn. Increased factor
productivity also raises the wages of skilled and unskilled workers and, as a result,
employment, output and income rse further. This contribution of employment growth
to the growth rate of income is measured by Xs-sd7t, and its strength depends on the
elasticity of the labour supply to changes in wages, X. Favourable domestic shocks
therefore raise growth rates in all countries by the same magnitude, i.e. (1+ X) E-dt.
To see how the competition bias determines how a country reacts to
domestic shocks, assume that 6 is finite and se=sfi=. As in the benchmark case,
favourable domestic shocks raise productivity equally in the a- and ,-industries,
raising wages, employment and output. This is captured by the term (1 + X) c-dn as
before. However, since the country is large in the markets for its a-products,
increases in the supply of a-products are met with reductions in prices that lower
production and income. This stabilizing effect of prices is measured by the term
-x. (1+ ) ) s dr. The more inelastic is the demand faced by each a-product (the
lower is 0) and the larger is the share of the a-industry (the larger is x), the more
important is this stabilizing role of prices. Since rich countries have larger a-
industries, domestic shocks have smaller effects on their growth rates, i.e.
( ) (~ O+X)
To see how the cyclical bias determines how a country responds to domestic
shocks, assume that 0--oo and s,*>Ka.
Turning to the data, Figure 3 plots the volatility and comovement of the
growth rate of the terms of trade against the log-level of income for a subset of
countries we used to construct Figure 1 (See also Table 2). Figure 3 suggests that
changes in the terms of trade are less volatile in rich countries than in poor ones,
and that changes in the terms of trade are more or less equally correlated with the
world cycle in rich and poor countries. If one is willing to assume that the theory is
approximately correct, one could read the top panel of Figure 3 as indicating that
E>>D, while the lower panel would show that M =0. These restrictions are consistent
with the notion that the cyclical biases are large (E>>D) but go in different directions
for different shocks (M =0).
However, this neither rules out nor confirms whether the cyclical bias is more
important than the competition bias in shaping the cross-section of business cycles.
36
On the one hand, one could point to the condition that E>>D to support the view that
the cyclical bias is more important than the competition bias. On the other hand, one
could stress that E>>D does not necessarily mean that D is small in absolute value,
and use the condition M=O to argue that the competition bias is more important than
the cyclical bias. In any case, given our very crude measures of the terms of trade,
we are reluctant to use Figure 3 to draw sharp conclusions regarding the relative
importance of our two hypotheses.
37
6. Concluding Remarks
We have developed two altemative explanations of the main features of the
cross-section of business cycles. Both explanations rely on the observation that the
law of comparative advantage leads rich countries to specialize in "high-tech"
products produced by skilled workers, while poor countries specialize in "low-tech"
products produced by unskilled workers. To the extent that "high-tech" and "low-
tech" industries respond differently to domestic and foreign shocks, business cycles
depend on the industrial structure of a country and, as a result, have different
properties in rich and poor countries. We have focused on two such asymmetries:
the competition bias and the cyclical bias.
Our work suggests some natural avenues for further research. On the
empirical front, the theory developed here provides a rich set of testable predictions
regarding the connection between the industrial structure of a country and the
nature of the business cycles that it experiences. To investigate the empirical validity
of these predictions, one would have to first identify asymmetries in how industries
react to domestic and foreign shocks. With this evidence in hand, it would then be
possible to quantify the extent to which cross-country differences in industry
structure contribute to cross-country differences in the properties of business cycles.
On the theoretical front, it is natural to ask how the possibility of cross-border
trade in financial instruments affects the shape of the cross-section of business
cycles. In the models presented here, the price of consumption in different dates
and states of nature varies across countries, creating an incentive for the
establishment of an intemational financial market that redistributes consumption
across dates and states. However, since neither factor supplies nor their
productivities depend on consumption, a redistribution of the latter cannot affect
output, although it certainly would affect consumption. If we want to construct an
argument relating financial integration to the shape of a cross-section of business
38
cycles, we need to link factor supplies and their productivities to consumption. One
way achieve this is to modify preferences so as to introduce income effects on the
labour supply. In our opinion, a preferred option would be to allow workers and firms
to invest in skills and technology, and then study how trade in financial instruments,
by affecting these investments, combines with commodity trade in shaping the
cross-section of business cycles.
39
References
Acemoglu, D. and F. Zilibotfi (1997) 'Was Prometheus Unbound by Chance? Risk,
Diversification and Growth," Joumal of Political Economy 105: 709-751.
Backus, D., P. Kehoe and Kydland (1995), "International Business Cycles: Theory
and Evidence" in T.F.Cooley (ed.) Frontiers of Business Cycle Research, Princeton
University Press.
Baxter, M. (1995), "Intemational Trade and Business Cycles" in G.M. Grossman and
K. Rogoff (eds.) Handbook of Intemational Economics, Volume 3, North-Holland.
Christiano, L., M. Eichenbaum and C. Evans (1997), "Sticky Price and Limited
Participation Models of Money: A Comparison", mimeo.
Corsetti, G and P. Pesenti (1998), 'Welfare and Macroeconomic Interdependence",
mimeo.
Davis, D. (1995), "Intraindustry Trade: A Heckscher-Ohlin-Ricardo Approach,"
Journal of International Economics 39: 201-226
Harrison, J.M. (1990), Brownian Motion and Stochastic Flow Systems, Krieger.
Kraay, A and J. Ventura (1997), 'Trade and Fluctuations", mimeo.
Obsffeld and Rogoff (1995), "Exchange Rate Dynamics Redux," Joumal of Political
Economy 103 (June):624-660.
Obsffeld, M. and K. Rogoff (1998), "Risk and Exchange Rates", mimeo.
40
Figure 1: Volatility and Comovement
Volatility
0.16
0.14
0.12
* 4
0.1
1 0.08
0.06
0.02
0 l l l l l l I
6 6.5 7 7.5 8 8.5 9 9.5 10
Iny
Comovement
0.8
U
gS0.2- '* ''*/
;, q,5 7 7.5 a s.sr 9 9.s 1 0
-0.2 . * *
-0.4
Iny
The top panel plots the standard deviation of the grovwth rate of real per capita GDP (diny) over the period
1960-1994 against the log-level of average per capita GDP in 1985 PPP dollars over the same period
(Iny), for a sample of 88 counteies. The bottom panel plots the correlation ot real per capita GDP growth
with world average per capita GDP growth. excluding the country in question (dInY) over the period 1960-
1994 against the log-level of average per capita GDP over the same period. All data are at annual
frequenoy. The sample consists of all non-OPEC market eoonomies with at least 30 observations on per
capita income (RGDPCH) beginning in 1960 in the Penn World Tables Version 5.6, extended to 1994
using constant price local ourrency growth rates from the World Bank World Tables.
Figure 2: Sample Paths of the Productivity Index
Country-Specific Variation Only
(0=0)
[I+2>F
2
Global Variation Only
A Time
IW VY IIII
2~~~
Both Country-Specific and Global Variation
t ~~~~~~~~~~~~~~Time
7- -
2
Figure 3: Volatility and Comovement of
Terms of Trade
Volatility
0.2
0.18 -
0.16
0.14
,0.12
: 0.1 -
0.08-..
0.06-
0.04
0.02
6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0
Iny
Comovement
0.6
0.5
0.4
0.3 4
* * *
0.2 .
t 0.1 . * * *
Z5 * 6
c.. 0 * I * ' 1 ' '. II
-0.1 , i.5 7 7.5 8 8.5 9. * 9.5 10
-0.21
* ~ ~~~ 4
-0.2
-0.3 -
-0.4
Iny
The top panel plots the standard deviation of the growth rate of terms of trade (dInT) over the period 1960-
1994 against the log-level of average per capita GDP in 1985 PPP dollars over the same period (Iny), for a
sample of 63 countries. The bottom panel plots the correlation of the growth rate of the terms of trade with
world average per capita GDP growth excluding the country in question (dInY) over the period 1960-1994
against the log-level of average per capita GDP over the same period. All data are at annual frequency.
Terms of trade growth is defined as the growth rate of the national accounts local currency export deflator
times the share of exports in GDP at constant local currency prices, less the growth rate of the
corresponding import deflator times the share of imports in GDP. The sample consists of all countries
with complete time series on these variables in the World Bank World Tables over the period 1960-1994.
Five countries for which terms of trade volatility was more than two standard deviations above the mean
for all countries were dropped from the sample (Argentina, Zambia, Israel, Bolivia and Nicaragua).
Table 1: Volatility and Comovement
Volatility Comovement
(Standard deviation of real (Correlation of real per
per capita GDP Growth) capita GDP growth with
world average excluding
country in question)
Average Correlation Average Correlation
with ln(per with ln(per
capita GDP) capita GDP)
Full Sample .051 -.621 .240 .627
(88 countries, 1960-94)
Full Sample, Non-Oil .050 -.624 .264 .539
Shock years
(88 countries, 1960-72,
1976-78,1982-94)
Full Sample, using -- -- .259 .440
unweighted world
average growth
Full Sample, using .097 -.431 .525 .428
deviations from linear
trend instead of growth
rates
Top Quartile by Income .031 -.573 .496 .425
Second Quartile .050 -.407 .260 .430
Third Quartile .051 -.094 .140 .297
Bottom Quartile .074 -.144 .066 .238
Note: See notes to Figure 1.
Table 2: Volatility and Comovement
of the Terms of Trade
Volatility Comovement
(Standard deviation of (Correlation of terms of
terms of trade growth) trade growth with world
average excluding country
in question)
Average Correlation Average Correlation
with ln(per with ln(per
capita GDP) capita GDP)
Full Sample .054 -.420 .054 .095
(63 countries, 1960-92)
Full Sample, Non-Oil .051 -.416 .044 -.257
Shock years
(63 countries, 1960-72,
1976-78,1982-92)
Full Sample, using -- -- .072 -.338
unweighted world
average growth
Full Sample, using .066 -.387 .211 -.330
deviations from linear
trend instead of growth
rates*
Top Quartile by Income .015 -.153 .072 -.563
Second Quartile .068 -.299 .074 .202
Third Quartile .069 .238 .053 -.263
Bottom Quartile .074 -.048 .006 .038
Note: See notes to Figure 3.
* For this row only, the level of the termns of trade is defined as a geometric average of the import and
export deflators, using the export and import shares in GDP as weights.
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