long-term growth and short-term economic instabilitycorresponding author. tel.: 33 1 44 58 28 76;...

*Corresponding author. Tel.: 33 1 44 58 28 76; fax: 33 1 44 58 28 80; e-mail: [email protected].

European Economic Review 44 (2000) 359}381

Long-term growth and short-termeconomic instability

Philippe Martin!,#,*, Carol Ann Rogers"! CERAS-ENPC, 28 rue des Saints Pe% res, 75007 Paris, France

" Department of Economics, Georgetown University, Washington, DC 20057, USA# CEPR, London, UK

Received 1 August 1997; accepted 1 May 1998

Abstract

When learning by doing is at the origin of growth the long-run growth rate should benegatively related to the amplitude of the business cycle if human capital accumulation isincreasing and concave in the cyclical component of production. Empirical evidencestrongly supports this "nding for industrialized countries and European regions. Usingthe standard control variables, we "nd that countries and regions that have a higherstandard deviation of growth and of unemployment have lower growth rates. The resultdoes not come from an e!ect of instability on investment. The negative relation, however,does not hold for non-industrialized countries, for which learning by doing may not to bethe main engine of growth. ( 2000 Elsevier Science B.V. All rights reserved.

JEL classixcation: O40; E32

Keywords: Growth; Business cycle; Learning by doing; Short-term economic instability

1. Introduction

A strong tradition among macroeconomists has been to study the businesscycle and long-term growth as two separate phenomena. Business cycle theorists

0014-2921/00/$ - see front matter ( 2000 Elsevier Science B.V. All rights reserved.PII: S 0 0 1 4 - 2 9 2 1 ( 9 8 ) 0 0 0 7 3 - 7

have considered long-term growth as an exogenous trend and growth theoristshave typically worked with models where short-term shocks have no impact onthe long-run growth rate of the economy.

A recent strand of literature has put together the two phenomena in a com-mon theoretical framework. From a theoretical point of view, the relationbetween short-term economic instability and long-run growth can be positive ornegative, depending on the mechanism at the origin of growth. As shown byAghion and Saint-Paul (1993), the sign of the relation depends on whether theactivity that generates growth in productivity is a complement or a substitute toproduction. In the case where they are substitutes, since the opportunity cost ofproductivity improving activities falls in recessions, a larger amplitude andfrequency of business-cycle #uctuations may have a positive e!ect on long-runproductivity and growth (Aghion and Saint-Paul, 1991). In the case of comp-lementarity, they show, as does Stadler (1990), that a positive (negative) shockwill have a positive (negative) long-term impact on productivity.

Two recent papers of ours (Martin and Rogers, 1995, 1997), in which growthis generated by learning by doing, belong to this class of models in whichproduction and productivity increasing activities are complements. We haveshown how stabilization policies could have a positive impact on human capitalaccumulation and, through this channel, on growth. A counter cyclical policythat smooths the impact of shocks on employment was needed for growthmaximization (Martin and Rogers, 1997) or welfare maximization (Martin andRogers, 1995). One natural implication from these models, that we did notdirectly address, was that short-term economic instability is detrimental forhuman capital accumulation and growth.

As theoretical models can predict a negative or a positive relation between theamplitude of the business cycle and growth, the next natural step should be toattempt to settle the question at the empirical level. One way to do this is to usetime-series methods to determine the long-run e!ect of macroeconomic shocks.Bean (1990) and Saint-Paul (1993) have found results that are mildly supportiveof a negative long-run e!ect of demand shocks on productivity. These time-series results may not be easy, however, to interpret in terms of the relationbetween long-term growth and the business cycle. The question of whethera positive shock has a positive or negative impact on the level of long-termproductivity is not the same as the question of whether the amplitude of thebusiness cycle is a determinant of the long-term growth rate. Even though theanswer to the "rst question is important from a positive point of view, it is notcertain what the policy implications are. On the other hand, if the amplitude ofthe business cycle has a negative impact on long-run growth, this has importantpolicy implications because it gives counter cyclical stabilization policies a newstrong role.

A more direct way to test the relation is to use cross-country regressions. Thispaper does so for OECD countries as well as for a sample of 90 European

360 P. Martin, C.A. Rogers / European Economic Review 44 (2000) 359}381

1They are interested in testing theories for which the negative relation between volatility andgrowth relies on uncertainty through the link of investment, see Bernanke (1983), Pindyck (1991) andRamey and Ramey (1991).

regions. Our empirical "ndings are the following. There is a strongly signi"cantand negative relation between growth and the standard deviation of growth,both for OECD countries and for European regions. This is true with di!erentsets of control variables and this is robust to di!erent speci"cations. In particu-lar, the relation is robust to the inclusion in the regressions of the investmentshare in GDP for industrialized countries. We also "nd that European regionsand industrialized countries with high standard deviation of unemploymenthave had lower growth which points to the key role of employment andpresumably learning by doing in this relation. However, we do not "nd that sucha relation holds for developing countries. This is consistent with Young's (1993)theoretical "nding that growth will be driven by learning by doing only atrelatively high levels of development.

Ramey and Ramey (1995) have found, in a sample of 92 countries as well as ina sample of OECD countries, that countries with higher volatility of growthhave lower growth. Their work focuses on the possible link between short-termuncertainty and long-run growth.1 In our work (Martin and Rogers, 1995,1997), we posit that learning by doing could generate a relation between growthand instability. The link between #uctuations and growth then would relyneither on uncertainty nor on an investment channel but on a labour channel sothat in our tests of this relationship in this paper, we will not di!erentiatebetween predictable and unpredictable shocks.

The next section of the paper describes the relation between instability andgrowth that we want to test, the methodology and the data. Section 3 reportsthe empirical results for European regions and OECD countries. Section 4 doesit for developing countries. An appendix outlines the simple theoretical modelthat informs our empirical tests.

2. Instability and growth

In an economy in which learning by doing is at the origin of growth, businesscycle #uctuations may reduce the growth rate. Recessions are periods in whichopportunities to learn by doing are foregone, so adverse business cycle shocksnegatively a!ect human capital accumulation. As long as employment andtherefore human capital accumulation is increasing and concave in the businesscycle disturbance, the &lost' learning is not fully regained when the cycle turnsupwards again. This is precisely the channel through which business cycle#uctuations can negatively a!ect long-term growth rates.

P. Martin, C.A. Rogers / European Economic Review 44 (2000) 359}381 361

2See the evidence on both of these aspects in the studies by Searle (1945), Rapping (1965) andBahk and Gort (1993).

3Watson (1992) shows that the least-squares growth rate is more robust to di!erences in the serialcorrelation properties of the data than the geometric rate of growth.

The appendix outlines a growth model with stochastic short-term productivityshocks. It shows that the amplitude of the business cycle a!ects negatively thegrowth rate if employment is concave in the output disturbances. The source ofthe disturbances (demand or supply) is not important for the conclusions, since allthat matters is that employment is increasing and concave in the disturbances. Wedo not test directly this concavity condition, but note that one of the stylized factsreported in the empirical literature on business cycles (see, for example, Danthineand Donaldson, 1993) is that labour productivity is pro-cyclical in all industrial-ized countries. The positive correlation between labour productivity (outputover employment) and output itself implies that employment increases withoutput but at a decreasing rate. Evidence also exists at the micro-level that, fordi!erent product lines, learning rates are initially high, declining over time asproduction cumulates. Lucas (1993) insists both on &how impressive the evidenceon the productivity e!ects of learning by doing can be' and on the shape of thelearning curve.2 This evidence of the learning curve suggests that learningincreases more rapidly when production is high (as it accumulates more rapidly)than when it is low but that this increase takes place at an decreasing rate.

We test the relation between growth and instability for three samples. The"rst sample consists of 90 European regions for which we have data between1979 and 1992. The second sample consists of the 24 industrialized countries forwhich we have complete data between 1960 and 1988. We test the relation bothfor the entire period and also for three sub-periods (1960}1969, 1970}1979 and1980}1988) that we pool together. The third sample consists of the 72 non-industrialized and non-oil-producing countries for which again we use databoth for the entire period and the three sub-periods already mentioned. Thedata for the European regions are taken from Eurostat and the data for the 96countries are from the World Bank and Barro and Lee (1993). As in Easterly,1994, the growth rates are computed by running a least-squares regression of thelogarithm of per capita GDP on time.3 The annual standard deviation of growthrates (SDGW) are taken as the measure of the amplitude of the business cycle.Alternatively, we could have chosen the variance of the growth rate. The resultsare very similar but in most cases produced a slightly better "t when using thestandard deviation. In addition, because the volatility of employment is a keyelement driving the theoretical results, we also use the standard deviation of theunemployment rate as an alternative measure of the amplitude of the businesscycle when using data for the European regions and the industrialized countries.The list of countries and European regions and their respective growth rates andstandard deviation of the growth rates are given in Tables 1 and 2.


Table 1Per capita growth rates and standard deviations of the growth rates for 97 countries

Countries Least-squares growthrates (1960}1988)

SDGW

Africa 1 Algeria 4.23 11.672 Chad !2.00 8.163 Egypt 5.15 6.214 Morocco 0.87 4.555 Botswana 8.06 7.926 Cameroon 3.77 5.457 Central Africa 0.56 3.888 Congo 2.07 6.899 Gabon 4.88 16.13

10 Gambia (The) 3.86 9.3411 Ghana !0.77 4.8212 Cote d'Ivoire 1.75 5.2913 Kenya 1.11 5.8314 Lesotho 5.60 8.9315 Liberia !0.50 7.2716 Madagascar !1.64 3.4617 Malawi 16.24 5.2818 Mali 1.23 5.1419 Mauritania 0.91 7.8820 Mauritius 0.69 5.6221 Mozambique !2.99 7.7322 Niger 0.73 7.8223 Nigeria !0.60 8.3424 Rwanda 2.90 9.3125 Senegal !0.71 4.3526 Sierra Leone 0.41 6.2527 Somalia 1.28 13.3628 Sudan !0.17 7.2129 Swaziland 0.24 8.7330 Tanzania 2.52 5.3031 Togo 2.75 6.0732 Tunisia 0.81 3.4433 Uganda 0.86 14.1534 Zambia !1.81 6.7235 Zimbabwe 2.88 6.23

Latin America 36 Barbados 0.98 5.3937 Costa Rica 0.53 3.5838 Dominican Rep. 1.53 6.6139 El Salvador !0.04 4.9540 Guatemala 1.83 2.8641 Haiti 0.77 4.2542 Honduras 1.67 3.7343 Jamaica 0.19 5.7244 Mexico 1.73 4.16


Table 1 (Continued)


SDGW

Latin America 45 Nicaragua !0.46 12.4446 Panama 3.12 3.8347 Trinidad 1.70 9.1548 Argentina 1.09 4.2649 Bolivia 1.40 4.5750 Brazil 3.29 8.8651 Chile 0.09 5.9552 Colombia 2.20 2.6453 Ecuador 3.16 4.9754 Paraguay 2.83 4.7855 Peru 0.71 5.7256 Uruguay 0.93 5.0457 Venezuela 1.54 6.76

Asia 58 Myanamar 2.19 5.6059 India 1.16 3.7860 Israel 2.74 4.5461 Jordan 2.98 8.0662 Pakistan 1.86 3.6863 Syria 4.41 10.3864 Korea 8.50 4.5565 Malaysia 3.41 4.7266 Philippines 2.19 3.9167 Singapore 4.77 4.3468 Taiwan 5.60 2.9969 Thailand 3.66 3.1570 Fiji 1.39 5.4271 Cyprus 3.79 9.4972 Malta 4.84 4.61

Western 73 Austria 3.27 2.4174 Belgium 2.38 2.5975 Denmark 1.70 2.8076 Finland 2.99 2.8977 France 2.70 2.1278 Germany 2.42 2.5879 Greece 4.23 4.0780 Iceland 2.20 4.0681 Ireland 2.45 3.2182 Italy 3.52 2.8483 Luxembourg 2.19 3.4384 Netherlands 2.04 2.4185 Norway 2.63 1.6286 Portugal 3.34 3.9487 Spain 3.09 4.0388 Sweden 1.64 1.78


4Even though we need to take into account of transitional dynamics in the empirical testsperformed in the next section, we do not conduct &convergence' tests so that our tests are not marredby Galton's fallacy as described by Quah (1993).

The other variables are the usual control variables that are used in growthregressions (see Barro, 1991; Levine and Renelt, 1992; Barro and Sala-i-Martin,1995). For all samples, the regression contains the initial GDP of the period(GDPI). The reason is twofold. First, the literature on conditional convergencehas shown that initial income is a determinant of growth. Second, if transitionaldynamics exist and resemble those of the Solow model they would give rise toa convergence result4 in the sense of a decline over time in per capita growthrates. This creates a bias in the relation between growth rates and the standarddeviation of growth rates. Suppose we look at two countries identical in alldimensions (in particular, in the underlying temporary shocks that hit theeconomy) except in their position with respect to the (common) steady state(de"ned as a situation where its growth rate is constant). The country furtheraway from the steady state will have a higher growth rate because of thetransitional dynamics. Its growth rate will also be decreasing faster than thecountry close to the steady state so that mechanically the variance of its growthrate will also be higher. This implies that the presence of transitional dynamicsin itself creates a positive relation between growth rates and the standarddeviation of the growth rates. It will therefore be especially important in ourgrowth regressions to account for the transitional dynamics so as to averta positive bias on the coe$cient of the standard deviation of growth.

In the cross-country regressions, we also have the average investment share inGDP (INV), the average share of government expenditures in GDP (SGOV),and the primary schooling (PRIM) from Barro (1991) which refers to the initialhuman capital. For European regions, secondary education (SEC) is also

Table 1 (Continued)


SDGW

Western 89 Switzerland 1.43 2.9390 Turkey 3.45 3.7291 United Kingdom 1.85 2.1792 Canada 1.36 2.9893 U.S.A. 1.11 2.5494 Japan 4.99 3.3095 Australia 1.58 2.5996 New Zealand 0.37 3.39


Tab

le2

Per

capita

grow

thra

tes

and

stan

dar

dde

viat

ions

ofth

egr

ow

thra

tes

for

90Euro

pean

regi

ons

Coun

trie

sR

egio

ns

Subre

gions

Lea

st-s

quar

eSD

GW

grow

thra

tes

(197

8}19

92)(%

)

SD

GW

(%)

Bel

gium

1V

laam

sG

ewes

t1.

672.

582

Reg

ion

Val

lone

1.05

2.41

3Bru

xelle

s1.

643.

164

Ant

wer

pen

1.37

3.08

5Bra

bant

1.32

2.66

6H

aina

ut0.

932.

567

Lie

ge1.

062.

618

Lim

burg

2.30

3.40

9Luxe

mbour

g1.

862.

1510

Nam

ur

1.19

2.75

11O

ost

-Vla

ande

rn1.

912.

9612

Wes

t-V

laan

der

en1.

542.

86

Den

mar

k13

Sjv

lland

-Lolla

nd

1.22

3.18

Fal

ster

-Born

holm

14Fyn

0.61

3.73

15Jy

lland

1.32

3.77

Deu

tsch

land

16Bad

enW

uer

ttem

berg

4.08

2.24

17Bay

ern

4.58

2.44

18Ber

lin2.

036.

7919

Bre

men

4.00

2.29

20H

amburg

4.10

4.33

21H

esse

n4.

702.

84


22N

iede

rsac

hse

n3.

962.

2623

Nord

rhei

nW

estfal

en3.

492.

1424

Rhei

nla

ndP

falz

3.62

2.74

25Saa

rlan

d1.

681.

8126

Sch

lesw

igH

olst

ein

3.71

2.48

Fra

nce

27Ile

deFra

nce

0.73

2.68

Bas

sin

Par

isie

n28

Cham

page

Ard

enne

0.00

3.59

29Pic

ardie

!0.

653.

3530

Hau

teN

orm

andi

e!

0.29

4.61

31C

entr

e0.

233.

3932

Bas

seN

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andi

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212.

9333

Bourg

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e0.

193.

1734

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Pas

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alai

s!

0.32

3.39

Est

35Lorr

aine

!0.

523.

8836

Alsac

e0.

243.

3737

Fra

nche

Com

te!

0.08

2.56

Oues

t38

Pay

sde

laLoire

0.16

3.20

39Bre

tagn

e0.

183.

2940

Poitou

Char

ente

s0.

193.

18Sud

Oue

st41

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tain

e0.

153.

7542

Mid

iPyr

enee

s0.

703.

4543

Lim

ousin

0.34

3.35

Cen

tre

Est

44R

hon

esA

lpes

0.20

3.13

45A

uve

rgne

0.20

2.96

Med

iter

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46Lan

gued

oc

Rouss

illon

0.18

3.79

47Pro

vence

Alp

esC

ote

d'A

zur

!0.

323.

52


Tab

le2

(Cont

inued

)

Coun

trie

sR

egio

ns

Subre

gions

Lea

st-s

quar

eSD

GW

grow

thra

tes

(197

8}19

92)(%

)

SD

GW

(%)

48Ir

elan

d1.

065.

09

Ital

iaN

ord

Ove

st49

Pie

mont

e!

0.15

3.90

50V

alle

d'A

osta

!0.

444.

0451

Lig

uria

!0.

223.

7552

Lom

bar

dia

0.12

4.62

Nord

Est

53Tre

ntin

oal

toad

ige

0.52

5.93

54V

enet

o0.

906.

1555

Friuli

Ven

ezia

Giu

lia

0.75

4.93

56Em

ilia

Rom

agna

!0.

095.

8757

Tosc

ana

!0.

144.

7658

Um

bria

!0.

155.

9259

Mar

che

0.32

7.24

60Laz

io0.

864.

8261

Cam

pan

ia0.

274.

76A

bru

zziM

olis

e62

Abru

zzi

0.79

5.84

63M

olis

e0.

836.

08Sud

64Pugl

ia0.

515.

6165

Bas

ilica

ta!

0.65

6.10

66C

alab

ria

0.11

6.69


67Sic

ilia

0.03

4.86

68Sar

degn

a0.

074.

19

69Luxe

mbour

g3.

486.

11

Ned

erla

nd

70G

ronin

gen

!0.

8810

.75

71Fries

land

3.08

3.76

72D

rent

he2.

224.

9173

Oost

2.86

2.75

Wes

t74

Utr

echt

3.06

4.49

75N

oor

dH

olla

nd

2.90

2.34

76Zuid

Holla

nd

2.77

3.30

77Zee

land

3.68

4.09

Zuid

78N

oor

dB

raban

t3.

543.

1279

Lim

burg

3.86

2.66

United

Kin

gdom

80N

ort

h0.

249.

4881

York

shire

0.45

8.47

82Eas

tM

idla

nds

0.64

9.42

83Eas

tA

nglia

1.15

9.34

84South

Eas

t0.

809.

4785

South

Wes

t0.

919.

4886

Wes

tM

idla

nds

0.43

8.16

87N

ort

hW

est

0.11

8.94

88W

ales

0.69

8.78

89Sco

tlan

d0.

529.

0990

Nort

her

nIr

elan

d0.

608.

78


5On the impact of political instability on growth see Alesina et al. (1992).6The standard errors in all the regressions reported in the paper are computed using White's

heteroskedacity-robust procedure.7Note that the coe$cient on the investment ratio has the wrong sign and is not signi"cant. In the

di!erent regressions we have performed the coe$cient on the investment ratio in the industrializedcountries is signi"cant and positive only when SGOV and PRIM are excluded from the regression.

included. For the developing countries, we include variables for political insta-bility. These are the average number of revolutions and coups (REVC) over theperiod and the number of political assassinations per million inhabitants(ASSP). The shocks we consider in our theoretical model could be thought of, asin Barro and Sala-i-Martin, as supply shocks and equivalent to temporarydeclines in the security of property rights. We include political variables in theregressions because we want to distinguish between economic and politicalinstability.5

3. Empirical evidence for European regions and industrialized countries

We now report results of the tests of the link between the amplitude of thebusiness cycle and the growth rate. Table 3 shows the results for the regressionsfor European regions between 1979 and 1992.6 We also include country dum-mies. The "rst and second column report regressions without and with sectoralshares: the share of agriculture in production (AGRI) and the share of industryin production (IND). In both cases, SDGW has the right sign and is verysigni"cant. We have checked that these results are not due to the presence ofoutliers.

Table 4 reports the results for the sample of 24 industrialized countries. The"rst two columns have the results for pooled data set whereas the next twocolumns report the growth rate between 1960 and 1988 as the dependentvariable. In all speci"cations, the coe$cient on SDGW is negative and signi"-cant at the 5% level or less. We have checked that the coe$cient on SDGWremains negative and signi"cant even when, in the pooled data set, we controlfor "xed time e!ects. The coe$cient becomes more negative and signi"cantwhen the investment ratio (INV) is included in the regression7 (see columns2 and 4). This shows that the impact of short-term instability does not gothrough an e!ect on investment which could be a natural alternative explana-tion to our empirical "ndings. This con"rms the results of Ramey and Ramey(1995). In their cross-country regressions, they "nd no relation between theinvestment share in GDP and the standard deviation of growth rates.

A further implication of our theoretical framework is that employmentinstability has a negative impact on long-term growth. To test this, we used, as


8For European regions, the instruments for the unemployment are a constant, the share ofindustry in regional output, the initial GDP and SDGW. For industrialized countries (pooled data),the instruments are a constant, the initial unemployment rate of the period, a dummy for the "rstdecade and the initial GDP. These instruments are unlikely to be caused by average growth rates.

an alternative measure of the amplitude of the business cycle, the standarddeviation of the unemployment rate (SDUN). The two columns M3N and M4N inTable 3 report for European regions the same regressions as in M1N and M2Nexcept that SDGW is replaced by SDUN. Because of lack of data on unemploy-ment, the growth rate for these regressions is computed on the period1983}1992. The coe$cient on SDUN is negative and signi"cant at the 5% level.We also tried as a measure of the e!ect of the business cycle on employment thestandard deviation of the unemployment rate of workers under 25 (SDUN25).We thought that young workers should have the steepest learning curve so thatthe e!ect of employment instability on growth should be stronger in this case.The results are given in columns M5N and M6N. The coe$cient has the right sign inboth regressions but is not very signi"cant whether sectoral shares are includedor not in the regression.

We also tested this implication of the model for OECD countries. In columnsM5N}M8N, the measure of the amplitude of the business cycle, the standard deviationof growth, SDGW, is replaced by the standard deviation of the unemploymentrate, SDUN. Again, as for the European regions the coe$cient has the expectedsign but is signi"cant at the 5% level only in the pooled data set.

Another implication of the channel we identify in the theoretical model is thataverage e!ective employment should have a positive impact on human capitalaccumulation and growth. Average unemployment rates are imperfect measuresof this e!ect but we nonetheless include them in our growth regressions to see ifthey have a negative impact on average growth. In all samples, the coe$cient(not reported) for the average unemployment rate is negative and very signi"-cant. There is, however, an obvious problem of reverse causality as averagegrowth rates are also a determinant of unemployment. To tackle this problem,we used two-stage least-squares regressions.8 We report these regressions incolumns M7N and M8N of Table 3 and columns M9N and M10N of Table 4. Inregression M7N of Table 3, where the instability measure is not included, theaverage unemployment rate (AVUN) has a strong negative and signi"cantimpact on growth in European regions. For industrialized countries (see regres-sion M9N in Table 4) this is also the case. When we include both the instabilitymeasure and the average unemployment rate in our regressions, the laterbecomes insigni"cant in the European regions sample and even though thecoe$cient on instability remains verysigni"cant, it decreases in both samples.This suggests that short-term instability and unemployment a!ect long-termgrowth in similar ways.


Tab

le3

Euro

pean

regi

ons

dep

enden

tva

riab

le:gr

ow

thra

te19

79}19

72(reg

ress

ion

M1N

and

M2N)

,gr

ow

thra

te19

83}19

92(reg

ress

ions

M3N

toM8

N)

M1N

M2N

M3N

M4N

M5N

M6N

M7N

M8N

No

ofob

s.TSLS

TSLS

9087

9087

8790

9090

Con

stan

t0.

0278

0.04

790.

0049

30.

0292

0.02

30.

0003

580.

0312

0.02

03[0

.31]

[0.0

4][0

.84]

[0.2

2][0

.34]

[0.9

9][0

.10]

[0.2

0]G

DP

I!

0.00

023

!0.

0001

9!

0.99

1!

1.02

]10

~6

!9.

94]

10~

7!

9.28

]10

~7

!2.

13]

10~

6!

9.17

]10

~7

[0.7

0][0

.78]

[0.2

05]

[0.2

2][0

.25]

[0.2

5][0

.00]

[0.0

7]PR

IM0.

6531

5!

0.01

150.

0158

0.00

279

0.00

448

0.01

360.

025

0.01

84[0

.97]

[0.5

8][0

.43]

[0.9

0][0

.85]

[0.4

9][0

.20]

[0.2

5]SE

C!

0.01

99!

0.02

59!

0.00

218

!0.

0182

!0.

0151

!0.

0037

7!

0.01

89!

0.01

62[0

.14]

[0.0

7][0

.51]

[0.1

7][0

.27]

[0.8

2][0

.10]

[0.1

2]A

GR

I!

0.01

97!

0.03

21!

0.00

5[0

.41]

[0.2

5][0

.13]

IND

!0.

0117

!0.

0152

!0.

0135

[0.1

1][0

.07]

[0.0

9]SD

GW

!0.

4255

!0.

4169

!0.

274

[0.0

0][0

.00]

[0.0

05]

SDU

N!

0.00

218

!0.

0021

2[0

.33]

[0.0

4]SD

UN

25!

0.00

0635

!0.

0008

21[0

.19]

[0.0

75]


AV

UN

!0.

230

!0.

084

[0.0

0][0

.20]

Bed

lux

!0.

0208

0.02

060.

0155

0.01

520.

0153

0.01

610.

0137

0.01

65[0

.005

][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

Ger

0.04

010.

0410

0.04

050.

040.

042

0.04

130.

0399

0.03

93[0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0]Fra

0.00

355

0.00

374

0.00

131

0.00

191

0.00

359

0.00

278

0.00

189

0.00

220

[0.6

2][0

.57]

[0.7

8][0

.66]

[0.4

9][0

.62]

[0.3

0][0

.30]

Ita

0.00

628

0.00

701

0.00

342

0.00

444

0.00

615

0.00

449

0.00

610.

0057

[0.3

7][0

.31]

[0.4

5][0

.31]

[0.2

3][0

.39]

[0.0

9][0

.09]

Ned

0.03

350.

0325

0.02

930.

0287

0.02

930.

0293

0.03

080.

0319

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

Uki

0.01

910.

0192

0.00

650.

0070

60.

0073

50.

0067

30.

0082

0.01

36[0

.01]

[0.0

0][0

.15]

[0.0

7][0

.12]

[0.2

0][0

.04]

[0.0

0]

R2

0.84

0.85

0.81

0.81

0.81

0.80

0.84

0.86

Not

e:T

he

sign

i"ca

nce

leve

lis

inbra

cket

s.


Tab

le4

Indu

strial

ized

coun

trie

sdep

ende

ntva

riab

le:g

row

thra

tes

1960}19

69;19

70}19

79;19

80}19

88an

dgr

owth

rate

1960}19

88

M1N

M2N

M3N

M4N

M5N

M6N

M7N

M8N

M9N

M10N

No.

7068

2423

6057

2423

6868

ofobs

.TSLS

TSLS

Cons

t.0.

296

0.30

70.

210

0.24

40.

265

0.28

40.

1845

0.18

260.

278

0.31

0[0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0]G

DP

I!

0.02

82!

0.02

87!

0.01

9!

0.02

3!

0.02

6!

0.02

64!

0.01

92!

0.01

99!

0.02

6!

0.02

8[0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0][0

.00]

[0.0

0]SG

OV

!0.

074

0.08

98!

0.04

04!

0.05

87!

0.01

21!

0.03

9!

0.04

37!

0.04

15!

0.05

18!

0.07

81[0

.041

][0

.02]

[0.2

2][0

.10]

[0.7

6][0

.45]

[0.1

1][0

.13]

[0.1

0][0

.02]

PR

IM0.

0002

0.00

030.

0001

50.

0003

0.00

031

0.00

019

0.00

028

0.00

040.

0002

0.00

02[0

.37]

[0.2

0][0

.36]

[0.0

1][0

.28]

[0.6

4][0

.00]

[0.0

0][0

.25]

[0.2

5]SD

GW

!0.

3768

!0.

4630

!0.

5334

!0.

7231

}}

}}

}!

0.41

3[0

.00]

[0.0

0][0

.03]

[0.0

0][0

.01]

SD

UN

}}

}}

!0.

0069

!0.

0071

!0.

0008

!0.

0005

5}

}

[0.0

0][0

.00]

[0.1

2][0

.29]

INV

}!

0.02

66}

!0.

0322

}!

0.04

05}

!0.

001

!0.

004

!0.

036

[0.2

5][0

.31]

[0.2

4][0

.97]

[0.1

0][0

.15]

AV

UN

!0.

001

!0.

001

[0.0

2][0

.025

]R

20.

530.

600.

670.

730.

590.

60.

740.

730.

580.

62

Not

e:The

sign

i"ca

nce

leve

lis

inbra

cket

s.


9This may be due to di!erent factors: our growth rates are calculated over the period 1960}1988rather than 1962}1985, using least square growth rates rather than geometric growth rates and witha slightly larger set of developing countries (72 in our sample, 68 in their sample, the di!erence beingmostly in African countries). A natural candidate explanation would also be that political instabilityvariables are included in our regressions but not theirs. However, we have checked that theirexclusion does not alter the results.

10We have also performed the same type of regressions with all countries, both developed anddeveloping. The coe$cient on the instability measure is positive and insigni"cant.

In both the European regions sample and the industrialized countries sample,the impact of short-term instability is quantitatively important. Lowering theinstability measure (SDGW) across European regions by one standard devi-ation is associated with an increase in the average growth rate of around halfa percentage point of annual per capita growth. For industrialized countries thisnumber is around 0.4 percentage point. Reducing the instability of the unem-ployment rate by one standard deviation has even a larger impact as it increasesthe average growth rate by 0.8}0.9 percentage point of annual per capita growthin European regions and around 0.6 percentage point for industrializedcountries.

4. Empirical evidence for developing countries

Table 5 reports results for non-industrialized countries. The "rst two columnsreport the regression results for the three decades pooled together without andwith the investment ratio. The next two columns report the same regressions forthe period 1960}1988. Except in one regression, the coe$cient on the instabilitymeasure is positive and insigni"cant. We "rst note that these results contradictthose of Ramey and Ramey whereas our results for the developed countries donot.9 We conclude that the negative relation between short-term instability andgrowth is robust only for the developed countries.10

There are several possible reasons for the di!erence in the relation betweengrowth and the standard deviation of growth in developed countries (Europeanregions and industrialized countries) and in developing countries.

(i) The lack of relation between the two variables in the developing countriescould be due to measurement error. To see whether our results are due tomeasurement errors, we reestimated our regression for the non-industrializedcountries sample using instrumental variables. The instruments for the standarddeviation of the growth rate are the standard deviation of the growth rate of thepreceding decade, the initial in#ation rate of the decade, the initial GDP percapita level and the number of revolutions and coups. This implied that wecould not use the observations of the "rst decade (1950}1960) when we usedthese instruments in the regression. The results did not change much. For this


Table 5Developing countires dependent variable: growth rates 1960}1969; 1970}1979; 1980}1988 andgrowth rate 1960}1988

M1N M2N M3N M4N M5N

No of obs. 224 205 72 67 70Const. 0.1643 0.1706 0.01763 0.168 0.12

[0.0000] [0.00000] [0.0012] [0.0003] [0.006]GDPI !0.0154 !0.0204 !0.0198 !0.0218 !0.0156

[0.0001] [0.0000] [0.0012] [0.00058] [0.0123]SGOV !0.0763 !0.0683 !0.0268 !0.0331 !0.0507

[0.0022] [0.0043] [0.325] [0.2168] [0.1774]PRIM 0.0001 0.0001 0.0004 0.0003 0.0001

[0.2822] [0.681] [0.0109] [0.0268] [0.2489]REVC !0.0117 !0.0012 !0.021 !0.0134 !0.0031

[0.0543] [0.8142] [0.0436] [0.1952] [0.5989]ASSP !4.7523 !6.835 7.2319 8.4874 !14.3062

[0.6585] [0.53078] [0.07061] [0.6392] [0.5987]SDGW 0.097 0.1516 0.0967 0.03962 0.0807

[0.2112] [0.0494] [0.2902] [0.7021] [0.4115]INV 0.148 0.1207 0.1268

[0.0000] [0.0018] [0.0016]

DAGL !0.0017[0.0034]

SUBAFRICA !0.0244 !0.0253 !0.0186 !0.01315 !0.0184[0.0000] [0.0000] [0.0263] [0.1366] [0.0209]

LAAM !0.0134 !0.0044 !0.0143 !0.0069 !0.0086[0.0101] [0.4116] [0.0143] [0.2855] [0.1797]

R2 0.17 0.26 0.29 0.30 0.28

Note: The signi"cance level is in brackets.

limited sample, the coe$cient became negative but only signi"cant at the 30%level so that this does not make a very convincing case that measurement erroris at the origin of our results for these countries.

(ii) Transitional dynamics should create a positive mechanical bias betweengrowth and the standard deviation of growth. We have checked that there isindeed a negative correlation between the initial level of GDP and the standarddeviation of growth rates. This bias will be more important the more importantthe role of transitional dynamics in explaining growth. It should therefore bestronger for developing countries. We have accounted for transitional dynamicsthrough the insertion in the regression of the initial level of GDP per capita andfor proxies for the steady-state levels of growth rates. Our results may re#ectthat not all the e!ect of transitional dynamics has been eliminated.


A related argument is that some of the high growth countries in our sample ofdeveloping countries may have been hit by important permanent shocks. Thiswill be the case for countries where industrialization has played an importantrole in growth. This would induce a positive bias between the standard deviationof growth and the growth rate. We have tested this hypothesis by adding in theregression the di!erence in the initial and "nal shares of agriculture in GDP(DAGL) in column M5N of Table 5. Unfortunately, we lose a lot of observationsbecause we do not have this data for all the countries of our sample. Introducingthis variable (which non-surprisingly is very signi"cant and negative) reduces thepositive coe$cient on the SDGW and also its signi"cance compared to regres-sion M2N. This may constitute weak evidence that developing countries have beenhit by important permanent shocks which obscure the relation between growthand the standard deviation of growth.

(iii) Our theoretical prediction should only hold in countries for whichgrowth is driven by learning by doing. In developing countries where theeconomy is dominated by traditional economic activities such as agriculture, thelearning curve can be thought as almost #at. In this case, our model predicts norelation between growth and the standard deviation of growth. Our empiricalresults are also consistent with Young's (1993) theoretical "nding that growthwill be driven by learning by doing only at relatively high levels of developmentthat is when the market size is large relative to the cost of invention.

(iv) If growth is driven by learning by doing then the level of employment iskey to our results. In particular, it is important that employment is procyclical.It is quite likely that in developing countries employment responds di!erently toshocks than in developed countries. In particular, contrarily to industrializedcountries, we have no stylized facts about the procyclical nature of employmentin developing countries. More generally, our results may simply re#ect the factthat the business cycle is an industrialized countries phenomenon.

It is di$cult at this stage to discriminate between these di!erent explanationswhich may all play a role. Our theoretical model coupled with Young's (1993)"nding that the learning by doing model should apply only at high developmentstages would predict that (iii) is enough to explain the empirical di!erencesbetween developed and developing countries.

5. Conclusion

We have studied the impact of learning by doing on the relation betweengrowth and short-term instability at the aggregate level. For developed coun-tries, our empirical results show a signi"cant and quantitatively importantnegative relation between growth and the amplitude of the business cyclewhether measured by the standard deviation of growth or the standard devi-ation of unemployment. We have seen that this relation does not work through


an impact of short-term instability on the level of investment in industrializedcountries which could be a natural explanation of the empirical results. Further-more, if investment played an important role in this relation it would be di$cultto explain the di!erence in results between developed and developing countries.If short-term instability is detrimental to investment it should be so in both setsof countries. Instead, our results are consistent with a model where humancapital accumulation is increasing and concave in production and Young's(1993) "nding that growth is driven by learning by doing only at high levels ofdevelopment.

Our conclusions have interesting policy implications. They give a clear andnovel rationale in favour of short-term stabilization policies, be they monetaryor "scal policies. Two recent papers of ours (Martin and Rogers, 1995, 1997)study in similar models under which conditions a "scal counter-cyclical policycan improve growth prospects. An interesting characteristic of this policyimplication is that it does not come out of a Keynesian-type model. In particu-lar, markets clear, and the origin of the shocks, supply or demand, does notmatter for the results.

Acknowledgements

We gratefully acknowledge the "nancial support of the Swiss Fonds Nationalde la Recherche Scienti"que. We thank an anonymous referee, Gilles Dowek,Hans Genberg, Pierre-Yves Geo!ard, Claire Lefevbre, Danny Quah and PierreVilla as well as seminar participants at CEPII for helpful comments and MarcoFugazza for excellent research assistance.

Appendix: A model of growth with learning by doing with stochastic shocks

A representative household chooses consumption ctand labour l

tover an

in"nite horizon to maximize the expected utility function:

E0;"E

0

=+t/0A

1

1#oBt ac1~1@p

t#(1!a)[h

t(1!l

t)]1~1@p

1!1/p. (A.1)

There is only one factor of production, labour, and goods and factor markets areperfectly competitive so that the income of the household is given by the level ofproduction and the household faces the budget constraints:

wtlt"c

t, t"0, 12, (A.2)

where wtis the wage rate. There is no saving. The maximum amount of labour

the consumer can supply in any date t equals unity: 04 lt41.


11For utility to be "nite, a su$cient condition is that lt((o#d)/b for all t.

Output is produced using e!ective labour, which equals hours worked (l)times human capital (h): y

t"/

tltht. The parameter /

tis an exogenous stochas-

tic productivity disturbance. The wage rate per unit of time worked is: wt"/

tht.

Human capital accumulates via learning by doing:

ht`1

"(1!d#blt) h

t, (A.3)

where d is the rate of depreciation of human capital, b is a parameter that tellshow much is learned through experience. According to Eq. (A.3), returns tolearning are not bounded and the rate of learning depends upon the #ow ofe!ective labour. All bene"ts of human capital accumulation are &external' that is,that individual workers do not internalize the fact that experience a!ects futurewages in the economy.

The business cycle is characterized by a two-state stationary Markov process.In good states the productivity level, /

t, takes the value G. In bad states, it takes

the value B(G. We thus assume that all disturbances are transitory. Thetwo-state Markov chain is de"ned by the following probabilities:

PrM/t`1

"G; /t"GN"P

G, PrM/

t`1"B; /

t"BN"P

B,

PrM/t`1

"G; /t"BN"1!P

B, PrM/

t`1"B; /

t"GN"1!P

G.

The long-term expected value of productivity is therefore:

E/t"

G(1!PB)#B(1!P

G)

1!j, (A.4)

where j"PG#P

B!1. We also assume that agents observe the state of the

economy at the beginning of the period.The optimal private choice of labour supply is then derived from the "rst-

order conditions of the maximization problem of the consumer:

lt"

1

1#k/1~pt

, (A.5)

where k"[(1!a)/a]p.11 The labour supply is increasing in the cyclical com-ponent of the wage rate } the productivity level } and will be procyclical if p ismore than 1. There are two levels of labour supply: l

Gand l

B.

The expected growth rate of output between date 0 and date ¹ is E0

(yT/y

0).

The average annual growth rate between those dates then equals

CE0AyT

y0BD

1@T. (A.6)


If the economy is the same states in dates 0 and ¹, then as ¹PR, thelong-run expected growth rate can be rewritten as

CE0AyT

y0BD

1@T"CE0A

/TlThT

/0l0h0BD

1@T"CE0A

hT

h0BD

1@T

"(1!d#blG)(1~PB)@(1~j) (1!d#bl

B)(1~PG)@(1~j). (A.7)

In Eq. (A.7), (1!PB)/(1!j) is the expected long-run proportion of good

states (when the growth rate equals (1!d#b1B)) and (1!P

G)/(1!j) is the

expected long-run proportion of bad states (when the growth rate equals(1!d#bl

G)). To determine the e!ect of the amplitude of the business cycle on

the expected long-run growth rate, we di!erentiate Eq. (A.7) with respect to theproductivity levels B and G. For the exercise to be meaningful, the change in theamplitude of the business cycle must leave the long-run expected level ofproductivity /

tunchanged. This requires that the changes in B and G satisfy the

condition

dB"!

1!PB

1!PG

dG. (A.8)

In this case, we obtain

dCE0AyT

y0BD

1@T

"bCE0AyT

y0BD

1@T 1!PB

1!j CLl

GLG

1

1!d#blG

!

LlB

LB

1

1!d#blBD. (A.9)

As long as the labour supply is increasing and concave in the productivity level,this expression is negative so that the expected long-run growth rate decreaseswith the amplitude of the business cycle.

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long-term growth and short-term economic instabilitycorresponding author. tel.: 33 1 44 58 28 76;...

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