FILE COPY MAecI i54 Losers and Winners in Economic Growth...FILE COPY I4341MAecI i54Losers and...

FILE COPY I4341MAecI i54

Losers and Winners in EconomicGrowth

RobartJ. Barro and Jong-Wha Lee

This paper examines the growth rates in 116 economies from 1965 to 1985. Itfinds that the lowest quintile had an average growth rate of real gross domesticproduct (CiDP) per capita of -1.3 percent, wbereas the highest quintile aver-rnged 4.8 percent. We isolate five influences that discriminate reasonably wellbetween slow and fast growers: a conditional convergence effect, whereby acountry grows faster if it begins with lower real GDP per capita in relation to itsinitial level of human capital in the forms of educational attainmentand healtb;a positive effect on growtb from a high ratio of investment to GDP (althoughthis effect is weaker than that reported in some previous studies); a negativeeffect from excessively large government; a negative effect from government-induced distortions of markets; and a negative effect from political instability.Overall, the fitted growth rates for eighty-five economies for 1965-85 had acorrelation of 0.8 with the actual values. We also find that female educationalattainment has a pronounced negative effect on fertility, whereas female andmale attainment are eacb positively related to life expectancy and negativelyrelated to infant mortality.

G rowth rates vary enormously across economics over long periods oftime. Figure 1 illustrates these divergences by showing the growth rateof real GDP per capita for 116 economies between 1965 and 1985. The

mean value is 1.7 percent a year, with a standard deviation of 2.2. The maxi-mum growth rate is 8.6 percent a year (for Singapore) and the minimum is -S0percent a year (for Kuwait). The lowest decile comprises twelve economies with

Robert J. Barro is professor of economics at Harvard University. Jong-Wha Lee is assistant professor ofeconomics at Korea University.

Proceedings of the World Bank Annual Conference on Development Economics 1993© 1994 The International Bank for Reconstruction and Development I mu WORW BANK 267

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.p'Futi. £wMuRate of Real GDP Per Capita, 1965-85

Growth rate (percent)10.0

8.0

6.0

4.0-~~-_

2.0 ___- -

0.0 _

-2.0

-4.0

-6.00 5 10 15 20

Number of countriesSour.e Summers and Heston 1988.

growth rates below -0.64 percent a year; the highest decile consists of thetwelve with growth rates of more than 4.35 percent a year. By quintiles, thetwenty-three economies with the poorest performance have growth rates below0.023 percent a year, and the twenty-three with the best performance havegrowth rates above 3.2 percent a year.

The difference between per capita growth of -1.3 percent a year (the averagefor the lowest quintile) and growth at 4.8 percent a year (the average for thehighest quintile) is that real GDP per capita falls by 23 percent over twenty years inthe former case and rises by 161 percent in the latter. Thus, for example, between1965 and 1985 real GDP per capita in two low-growth countries, Jamaica andSudan, fell from $1,807 and $729 (in 1980 U.S. dollars), respectvely, to $1,725and $540. Over the same period, real CDP per capita for two high-growth coun-tries, Botswana and Korea, rose from $530 and $797, re-pectively, to $1,762 and$3,056. Thus, even over periods as short as twenty years, the variations in growthrates caused dramatic differences in average living standards.

268 Losers and Wirnners in Economic Growth

The key challenge for economists is to understand why growth outcomesdiffer so much and to use this knowledge to recommend policy changes thatincrease the chances of the lagging countries to perform better. This challengedefines the objectives of this paper and of our ongoing research.

We build in this work on previous cross-country empirical analyses to isolatemajor determinants of growth rates for about 100 economies for 1965-75 and1975-85. We then summarize these results in a framework of sources of growthto show how the explanatory variables for the economies in the lowest quintileof growth rates differed systematically from those in the highest quintile. Wedescribe our findings in terms of five key determinants of growth: first, a condi-tional convergence effect, whereby a country grows faster if it begins with lowerreal CDP per capita in relation to its initial level of human capital in the forms ofeducational attainment and health; second, a positive effect on growth from ahigh ratio of investment to GDP (although this effect is weaker than that reportedin some previous studies); third, a negative effect from overlarge government,represented by the ratio of government consumption (exclusive of defense andeducation) tO GDP; fourth, a negative effect from government-induced distor-tions of markets, proxied by the black-market premium on foreign exchange;and fifth, a negative effect from political instability, represented by the propen-sity to experience revolutions. Overall, the fitted growth rates for 1965-85 thatare derived from these five influences have a correlation of about 0.8 with theactual growth rates. These explanatory variables go a long way toward deter-mining whether a country ends up in the low- or high-growth group.

The final sections of the paper provide preliminary evidence on the determi-nants of fertility and health as measured by life expectancy and infant mortality.Our results confirm the key role of female education in generating r-eductions infertility and hence in population growth. We also find that female and maleattaimment are each positively related to life expectancy and negatively related toinfant mortality.

Losers and Winners, 1965 to 1985

The growth rates of real GO)P per capita shown in table 1 are those reported bySummers and Heston (1988, 1991) and the World Bank. Of the twenty-threeeconomies in the lowest quintile of growth in Summers and Heston (1988),sixteen are in Sub-Saharan Africa, five in Latin America, and two in the MiddleEast. The makeup of the lowest quintile is similar in both the updated figuresfrom Summers and 1-eston (1991) and in the World Bank data. Summers andHeston (1991) find that seventeen of the twenty-four countries in the lowestquintile of growth rates are in Sub-Saharan Africa. Most of these countries agreewith the 1988 list. In the World Bank figures, fourteen of the twenty-twoslowest growers are in Sub-Saharan Africa. Table 2 provides a parallel treatmentof winners showing that the composition of this list, too, is similar in thealternative data sources.

Barn and Lee 269

Table 1. Economies in the Lowest Quintile of Growth, 1965-8S

Sum,ners-Heston 1988 Summers-Heston 1991 World fank

Growth Growvth GrowthEconomy rat Ecanmy mtid Economy rate

Angola -0.025 Afghanistan -0.003 Afghanistan -0.003Benin -0.009 Angola -0.028 Angola -0.021Central African Rep. -0.003 Argentina 0.001 Bangladesh 0.002Chad -0.033 Benin -0.004 Central African Rep. 0.000El Salvador -0.004 Central African Rep. -0.004 Chad -0.022Ethiopia -0.002 Chad -0.016 El Salvador 0.000Ghana -0.021 Gambia, The -0.010 Ghana -0.014Guinea 0.000 Ghana -0.012 Haiti 0.001Guyana -0.003 Guinea 0.000 Jamaica 0.000Iraq -0.006 Guyana -O.0O5 Kuwait -0.062Jamaica -0.002 Kuwait -0.065 Liberia -0.005Kuwait -0.050 Liberia -0.008 Madagascar -0.014Liberia -0.006 Madagascar -0.018 Mauritania 0.003Madagascar -0.011 Mauritania -0.002 Nicaragua -0.008Mozambique -0.025 Mozambique -0.029 Niger -0.020Nicaragua -0.005 Nicaragua -0.018 Senegal -0.003Senegal -0.003 Niger -0.007 Sudan 0.003Somalia -0.006 Papua New Guinea 0.000 Tanzania 0.002Sudan -0.015 Peru 0.001 Uganda -0.027Togo -0.006 Senegal -0.004 Venezuela -0.011Venezuela -0.030 Sierra Leone -0.008 Zaire -0.012Zaire -0.019 Sudan 0.002 Zambia -0.011Zambia -0.019 Zaire -0.011

Zambia -0.022.

Note: For Summers and Heston 1988 and 1991, growth rates refer to purchasing-power-adjusted real GDP percapita between 1960 and 1985. For WVorld Bank data, values are own-ountry real growth rates from the WorldBank database. For Guinea, Guyana, Iraq, and Mozambique, World Bank data were not available. Afghanistanis not included in the Summers and Heston 1988 data.

Sources: Summers and Heston 1988, 1991; World Bank Economic and Social Database, National AccountsData.

We focus here on the losers and winners in the Summers-Heston 1988 data1

and try to isolate some of the factors that determine growth and thereby affectthe probability that a country will turn out to be a loser or a winner. Tables 3and 4 provide a preliminary overview of the prospects for this enterprise. Thetables indicate whether the country is in the regression sample for growth: of thetwenty-three low-growth countries, fourteen are induded for 1965-75 and fif-teen for 1975-85; of the twenty-three high-growth economies, twenty areinduded for 1965-75 and twenty-two for 1975-85. The fitted values for 1965-75 and 1975-85 show how much of the growth rates can be explained by thedecadal regressions.

For 1965-85, the average growth rate for the slow growers is -0.013 a yearand the average of the fitted values is -0.002 a year. In contrast, for the fast

270 Losers and Winners in Economic Growth

Table 2. Economies in the Highest Quintile of Growth, 1965-8S

Surnmers-Hesron 1988 Stnmtmers-Heston 1991 World Bank

Economny Growth rate Ecopnomy Growth rate Economy Growth rate

Austria 0.032 Algerin 0.036 Barbados 0.033Barbados 0.044 Botswana 0.077 Botswana 0.087Botswana 0.060 Brazil 0.035 Brazil 0.037Brazil 0.043 Cameroon 0.039 Burundi 0.034Cameroon 0.034 China 0.051 Congo O.OS7Cyprus 0.042 Congo 0.049 Cyprus O.OS0Ecuador 0.033 Cyprus 0.041 Egypt 0.036Gabon 0.044 Egypt 0.052 Finland 0.034Greece 0.037 Gabon 0.041 Greece 0.039Hong Kong 0.061 Greece 0.03S Hong Kong 0.066Indonesia 0.048 Hong Kong 0.056 Indonesia 0.045Japan O.OS1 Indonesia 0.049 Italy 0.034Korea, Rep. of 0.067 Japan 0.045 Japan 0.052Lesotho 0.046 Korea, Rep. of 0.061 Korea, Rep. 0.068Malaysia 0.048 Lesotho 0.060 Malaysia 0.046Malta 0.068 Malaysia 0.041 Malta 0.077Norway 0.036 Malta 0.060 Norway 0.036Portugal 0.035 Portugal 0.037 Portugal 0.040Rwanda 0.040 Rwanda 0.038 Saudi Arabia 0.034Singapore 0.086 Singapore 0.068 Singapore 0.080Taiwan (China) O.OS Syria 0.037 Thailand 0.044Thailand 0.041 Taiwan (China) 0.060 Tunisia 0.036Tunisia 0.040 Thailand 0.037

Tunisia 0.034

Note: See note to table 1. World Bank data are not available for China or Taiwan. China. China is notincluded in the Summess and Heston 1988 data.

Sourees: Summers and 1Heston 1 9B8 , 1991; World Bank Economic and Social Database, National AccuntsData.

growers the average growth rate is 0.048 a year and the average of the fittedvalues is 0.039 a year. The typical fast grower therefore grew by 6.1 percentagepoints a year more than the typical slow grower, and 4.1 percentage points ofthis gap, on average, was captured by the fitted values. Hence the fitted valuesshow a wide difference between the slow and fast growers, and it is worthwhileto assess the factors that underlie the differences between the two groups. (Forall eighty-five economies in the regressions the correlation between the actualand fitted growth rates for 1965-85 is 0.81.)

Tables 3 and 4 also show projected growth rates for 1985-95. The average ofthese projected values for the slow growers is 0.008 a year and that for the fastgrowers is 0.033 a year. In other words, the model predicts that the average gapbetween the two groups will decline from 6.1 percentage points for 1965-85 to2.5 percentage points for 1985-95. Thus the distinction between slow and fastgrowers is expected to lessen but to persist to a significant extent. 2

Barro and Lee 271

Table 3. Regression Resultsfor Low-Growth Economies

Projected Summers-.eston World BankGrowth PFtted Growth Fitted Growth Fitted growth 1991 growth

rate, value, rate, value, rate, value, rate. growth rate, rate,Economy 196S-85 196S-85 196S-75 196S-7S 1975-85 1975-85 1985-95 1965-85 196S-85Angola, -0.025 - -0.032 - -0.018 - - -0.028 -0.021Benin -0.009 0.014 -0.007 0.011 -0.012 0.019 0.038 -0.004 0.003Ccntral African Rep. -0.003 0.003 0.006 0.008 -0.012 -0.002 0.017 -0.004 0.000Chad, -0.033 (-0.022) -0.007 (-0.026) -0.060 (-0.017) (0.012) -0.016 -0.022El Salvador -0.004 -0.002 0.014 0.009 -0.021 -0.014 -0.011 0.004 0.000Ethiopias -0.002 (0.013) 0.003 (0.025) -0.006 (0.001) (0.018) 0.004 0.005Ghana -0.021 -0.006 -0.006 0.028 -0.035 -0.037 -0.039 -0.012 -0.014Guineaa 0.000 - -0.010 - 0.010 - - 0.000Guyanab -0.003 (0.008) 0.029 (0.026) -0.036 -0.010 -0.014 -O.OOSIraq -0.006 -0.012 0.021 -0.O11 -0.033 -0.012 0.020 0.005Jamaica -0.002 0.020 0.024 0.043 -0.028 -0.003 0.014 0.004 0.000Kuwaitc -O.OSO -0.029 -0.058 -0.036 -0.043 -0.023 0.009 -0.065 -0.062Liberia -0.006 0.012 0.020 0.016 -0.033 0.008 0.028 -0.008 -0.005Madagascara -0.011 (0,013) -0.003 (0.020) -0.018 (0.006) (0.029) -0.018 -0.014Mozambique' -0.025 (-O.OSI) -0.009 (-0.030) -0.040 (-0.072) (-0.033) -0.029Senegal -0.003 -0.004 -0.005 -0.001 -0.001 -0.006 0.003 -0.004 -0.003Somaliaa -0.006 (-0.008) 0.003 (-0.003) -0.016 (-0.012) (0.011) 0.007 0.007Sudan -0.015 -0.017 -0.012 -0.015 -0.018 -0.017 0.004 0.002 0.003Togo -0.006 0.024 0.019 0.027 -0.030 0.023 0.050 0.007 0.012Venezuela -0.030 0.002 -0.028 0.005 -0.032 -0.002 0.013 0.006 -0.011Zairc -0.019 0.009 0.015 0.023 -O.052 -0.001 0.028 -0.011 -0.012Zambia -0.019 -0.006 0.003 0.009 -0.041 -0.022 0.001 -0.022 -0.011Mean -0.013 -0.002 0.001 0.006 -0.028 -0.013 0.008 -0.008 -0.007Number of obscnrations 21 21 21 21 21 21 21 21 is

- Not available.Note: The economics selcuted are thosc in the lowest and highest quintiles for growth rates of real GDP per capita from 196S to 1985, according to Summers and Heston 1998

(version 4); see table 1. The fitted values are from the regressions in column 2, table S. The projected growth rates for 1985-9S are also based on these regressions. The numbers inM parentheses are fitted values for economies that were not included in the regressions. These fitted values are based on estimates of missing data on one or more explanatoryr- variables.

a. Not included in the growth regressions shown in table 5.b. Included in the growth regressions for the second decadal sample but not the first.c. Data are available, but the economy was excluded from the regressions.Sources: Summers and Heston 1988, 1991.

Table 4. Regression Resultsfor High-Growth Economies0 Projected Suizmm r.Hezton wormd funka Growtb Fitted Growth Fitted Growth Fitted growth 199; groth

rate, value, rate, valhie. rate, valuiL. raft, giouis rate, rite,R Economy .96S-8S 1965-85 1965-7S 1965-75 197S-8S 197S-85 1985-9S 196S-8S 1965-ASAustria 0.032 0.032 0.041 0.028 0.024 0.035 0.036 0.030 0.032Barbadosa 0.044 (0.030) 0.048 (0.046) 0.039 0.019 0.028 0.025 0.033Botswana 0.060 6.026 0.74 0.039 0.046 0.013 0.020 0.077 0.087Brazil 0.043 0.025 0.062 0.038 0.024 0.012 0.015 0.035 0.037Cameroon 0.034 0.026 0.031 0.028 0.037 0.026 0.032 0.039 0.030Cyprus' 0.042 (0.048) 0.008 (0.056) 0.076 0.043 0.032 0.041 0.056Ecuador 0.033 0.021 0.055 0.035 0.010 0.008 0.018 0.029 0.033Gabonb 0.044 - 0.088 - 0.000 - - 0.041 0.030Greece 0.037 0.048 0.057 0.060 0.017 0.034 0.042 0.035 0.039Hong Kong 0.061 0.055 0.051 0.065 0.070 0.045 0.043 0.056 0.066Indonesia 0.048 0.031 0.042 0.033 0.0S4 0.031 0.034 0.049 0.045Japan 0.051 0.050 0.065 0.063 0.037 0.040 0.046 0.045 0.052Korea, Rep. of 0.067 0.055 0.083 0.058 0.051 O.OS3 0.060 0.061 0.068Lesotho 0.046 0.028 0.056 0.043 0.036 0.014 0.018 0.060 0.026Malaysia 0.048 0.047 0.054 0.049 0.042 0.045 0.055 0.041 0.046Malta 0.068 0.046 0.083 0.065 0.054 0.026 0.024 0.060 0.077Nonvay 0.036 0.029 0.032 0.034 0.039 0.027 0.023 0.033 0.036Portugal 0.035 0.033 0.050 0.050 0.020 0.015 0.026 0.037 0.040Rwanda 0.040 0.038 0.083 0.063 -0.003 0.016 0.024 0.038 0.021Singapore 0.086 0.064 0.086 0.077 0.087 0.051 0.038 0.068 0.080Taiwan (China) 0.058 0.056 0.058 0.064 0.057 0.047 0.051 0.060Thailand 0.041 0.041 0.045 0.051 0.037 0.033 0.036 0.037 0.044Tunisia 0.040 0.024 0.049 0.028 0.030 0.021 0.035 0.034 0.036Mean 0.048 0.039 0.055 0.049 0.040 0.030 0.033 0.045 0.047Number of observations 22 22 22 22 22 22 22 22 21

- Not available.Note: The economies selected are those in the highest quintile forgrowth rates for real GDP per capita from 1965 to 1985 according to Sunmunes and Heson 1958; see table 2. See

also note to table 3.a. Included in the growth regressions for the second decadal sample but not the first.b. Not included in the growth regressions shown in table S.

tA Source: Summers and Heston 1981,1991.

Setup of the Empirical Analysis of Growth RatesIn this section wc provide the regression results that underlie the fitted valuesand projections in tables 3 and 4. Our sample includes ninety-five economies(see the appcndix), chosen on the basis of the availability of data.3 We studygrowth rates over two decades, 1965-75 and 1975-85. Thus our panel data setincludes a limited amount of time-series variation.

The basic empirical framework relates real per capita growth to two vari-ables: (a) initial levels of state variables, such as the stock of physical capital andthe stock of human capital in the forms of educational attainment and health;and (b) control or environmental variables (some of which are chosen by govern-ments or private agents), such as the atio of government consumption to GDP,

the ratio of domestic investment to GDP, the fertility rate, the black-marketpremium on foreign exchange, changes in the terms of trade, measures of politi-cal instability, tariff rates, and so on.

One of the state variables that we use is the measure (or measures) of schoolattainment constructed by Barro and Lee (1993). We use standard numbers onlife expectancy at birth to represent the initial level of health. The available dataon physical capital seem unreliable, especially for developing countries and wenin relation to the measures of human capital, because they depend on arbitraryassumptions about depreciation and on inaccurate mneasures of benchmarkstocks and investment flows. As an alternative to using the limited data that areavailable on physical capital, we assume that for given values of schooling andhealth a higher level of initial real cDP per capita reflects a greater stock ofphysical capital per person (or a larger quantity of natural resources). Therefore,for given values of the contemporaneous determinants of growth, we write afunction for the per capita growth rate in period t, Dye, as

(,1) Dyt = F(y,-,, e,_I. h,-,;.*.*. )

where Yt-I is initial per capita GDP, ets_I is initial schooling per person, b,-, is ameasure (life expectancy) of the typical person's health, and . . . denotes thearray of control and environmental influences that are being held constant.

If there are diminishing returns to reproducible factors, as in the usual neo-classical growth model for a closed economy (Solow 1956; Cass 1965; Koop-mans 1965), then an equiproportionate increase in Y t-i, et-,, and hX., wouldreduce Dy, in equation 1.4 However, a number of theories, summarized in Barroand Sala-i-Martin (1993, ch. 4), suggest important influences on growth fromimbalances between physical and human capital. For example, a high ratio ofhuman capital (e 1.. 1 or h_ I in equation 1) to physical capital is likely to inducerapid growth in physical capital and output, as in the aftermath of a war thatdestroys primarily physical capital. Other theories have stressed the positiveinfluences of human capital on the ability to absorb new technologies; hence Dy1would rise with et,1 and bt.. on this count. Thus, although t;.e influence ofYt-i on Dy, in equation 1 would be negative, the effects of et_] and h,_1 arelikely to be positive.


In the basic regression that we consider below, the control and environmentalvariables are the ratio of real gross domestic investment to real GDP, denoted as1/ Y; the ratio of government consumption (measured net of spending on defenseand education) to GoP, denoted as GIY; the black-market premium on foreignexchangc, entered as log(1 + premium); and the average number of revolutionsa year for the country. We take account of the likely endogencity of thesevariables by using lagged values as instruments.

In the neoclassical growth model the effects of the control and environmcntalvariables on the growth rate can be ascertained from their effects on the steady-state position. For example, a higher value of I/ Y raises the steady-state ratio ofoutput to effective worker; the growth rate, Dy,, accordingly tends to rise forgiven valu:s of the state variables. Similarly, if GIY does not directly affectproductivity but is associated with greater distortions (because of the govern-ment activities themselves or because of the associated public finance), a higherGIY implies a lower steady-state position and hence a lower growth rate forgiven values of the state variables.

We view the black-market premium on foreign exchange as a proxy for mar-ket distortions, whether attributable to exogenous government policies or toreactions to external shocks such as changes in the terms of trade. (The black-market premium is also a desirable variable because it is objectively measurableand widely available.) Thus we anticipate that a higher black-market premium,like other governmental distortions, lowers the steady-state level of output pereffective worker and therefore reduces the growth rate for given values of thestate variables.

We view an increase in political instability, represented, for example, by thepropensity to experience revolutions, as equivalent to a decline in the security ofproperty rights. As with an increase in tax rates or other governmental distor-tions, the worsening of property rights tends to lower the steady-state level ofoutput per effective worker and consequently to reduce the growth rate for givenvalues of the state variables.

Educational Attainment

The data on educational attainment (Barro and Lee 1993) begin with informa-tion on of the adult population (aged 25 and over) by sex and by level ofschooling-no schooling, incomplete and complete primary schooling, incom-plete and complete secondary schooling, and incomplete and complete tertiaryschooling.5 The data are, however, more plentiful for the four-way dassifica-tion that does not distinguish incomplete from complete education at each level.

The census information fills about 40 percent of the possible cells for a paneldata set that consists of more than 100 economies observed at five-year intervalsbetween 1960 and 1985. We use information on adult illiteracy to expand thecoverage of the no-schooling category beyond this 40 percent figure. Theremaining cells were filled at the four-level classification by a perpetual-

Barro and Lee 275

inventory method. This method treats the census values as benchmark stocks:end LIscs laggcd valucs of school cnrollmcnt ratios to measurc the flows ofpersons inito the various categories of attainment. This procedure introduceserrors because the enrollment ratios arc well known to be unreliable; for exam-pie, the nvailable data are mainly gross figures that overcount school repeaters.Our use of census figures does, howevcr, minimize the reliance on the enroll-ment numbers. The brcakdown into incomplete and complete attainment ateach lcvel is based on the limited information that is available about completionpercentages.

The data on school attainment at the various levels are used to measure theavcrage years of attainment by sex for cach country and for each date (at five-year intcrvals). This construction takes account of the variations across coun-tries in the typical duration of primary and secondary schooling. We shouldstress that the data do not allow for differences in the quality of schooling acrosscountries or over time. We believe that no useful measures of quality are avail-able for the broad sample that we are using; information is widely available onlyon pupil-teacher ratios and public spending on education.

Regression Results for Growth Rates

Table 5 shows the results of the regression for eighty-five economies for 1965-75 and ninety-five economies for 1975-85. Column t estimates by the seem-ingly unrelated (SUR) technique, which allows for country random effects thatare correlated over time. Note, however, that the correlation of the residualsfrom the growth-rate equations across the two time periods is essentially zero.We discuss estimation by instrumental procedures later.

The variable Iog(GDP) is an observation for 1965 in the 1965-75 regressionand for 1975 in the 1975-85 regression. The estimated coefficient, -0.0264(s.e. = .0030), shows the tendency for conditional convergence that has beenreported in previous studies, such as Barro (1991). The convergence is condi-tional in that it predicts higher growth in response to lower starting GDP perperson only if the other explanatory variables (some of which are highly corre-lated with GDP per person) are held constant. The magnitude of the coefficientimplies that convergence occurs at the rate of 3.1 percent a year.6

The school attainment variable that turns out to be positively related tosubsequent growth is years of male secondary schooling, observed in 1965 and1975, respectively. The estimated coefficient, 0.0134 (s.e. = 0.0041), meansthat an additional year of secondary schooling raises the growth rate by 1.34percentage points a year. The mean of male secondary schooling was 0.73 years(s.d. = 0.69) in 1965, and 1.05 (s.d. = 0.94) in 1975.

A puzzling finding, which tends to recur, is that the initial level of femalesecondary education enters negatively in the growth equations; the estimatedcoefficient is -0.0084 (s.e. = 0.0045). One possibility is that a high spreadbetween male and female secondary attainment is a good measure of backward-


ness; hence, less female attainment signifies more backwardness and accordinglyhigher growth potential through the convergence mechanism.

We measure life expectancy at birth by an average of values prevailing overthe Five years prior to the start of each decade: 1960-64 in the first case and1970-74 in the second. (The results arc essentially the same if the valuesreported for 1965 and 1975 are used instead.) The variable is entered in theform log(life expectancy). This variable is highly significant in the growthregressions: the estimated coefficient is 0.0727 (s.c. - 0.0132). The mean of thelife-expectancy variable was 3.99 (s.d. - 0.21) in 1965, corresponding to amean life expectancy of 55.4 years (s.d. - 11.7), and 4.05 (s.d. - 0.20) in1975, or a mean life expectancy of 58.7 years (s.d. - 11.2). Therefore, in the1965-7S equation, a one-standard-deviation increase in life expectancy is esti-mated to raise the growth rate by 1.5 percentage points a year.

It seems likely that life expectancy has such a strong positive relation withgrowth because it proxies for features other than good health that reflect desir-able performance in a society. For example, higher life expectancy may go alongwith better work habits and a higher level of skills (for given measured values ofper capita product and years of schooling).

Using Summers and Heston (1988) data, the ratio of real gross domesticinvestment to real GDP, II Y, is entered into the regressions as a decadal average

Table 5. Regressionsfor Growth Rate of Real GDP Per Capita (Part 1)

3SL5 3SLS 3SLS(inrstrumentfor (law- (bigh-

StIR 3SLt revolution) ineome) income)Variable (1) (2) (3) (4) (5)

Log (GDP) -0.0264 -0.025S -0.0229 -0.0239 -0.02SS(0.0030) (O 003S) (0.0038) (0.0078) (0.0052)

Male secondary 0.0134 0.0138 0.01SO 0.0234 0.0089schoolinig (0.0041) (0.0042) (0.0042) (0.0104) (0.0048)

Female secondary -0.0084 -0.0092 -0.0109 -0.0270 -0.0047schooling (0.0045) (0.0047) (0.0047) (0.0217) (0.00SO)

Log (life 0.0727 0.0801 0.0733 0.0850 0.0049expectancy) (0.0132) (0.0139) (0.O150) (0.0242) (0.0268)

fIfY 0.120 0.077 0.084 0.071 0.137(0.020) (0.027) (0.030) (0.051) (0.038)

GI'Y -0.170 -0.15 -0.151 -0.148 -0.086(0.026) (0.034) (0.035) (0.052) (0.050)

Log(1 + black- -0.0279 -0.0304 -0.0245 -0.011 -0.0786market premium) (0.0048) (0.0094) (0.0090) (0.0091) (0.0188)

Revolutions -0.0171 -0.0178 -0.0158 -0.0199 -0.0188(0.0082) (0.0089) (0.0113) (0.0159) (0.0120)

R2 (number of 0.57 (85) 0.56 (85) 0.47 (68) 0.56 (41) 0.56(44)observations) 0.58 (95) O.S6 (9S) 0.57 (94) 0.53 (48) 0.58 (47)

Serial correlation 0.01 0.01 0.00 0.01 0.03

(continued)

Baro and Lee 277

TableS (Part 2)

i VIJ .MU .I3SLS iISS iSLE iSiLVarisbil (6) (7) (8) (9) (10) (31)

Log (ant') -0.0253 -0.0247 -0.0252 -0.025O -0.02S7 -0.0258(0.0035) (0.003S) (0.0036) (0.0036) (0.0035) (0.0036)

Male secondary 0.0149 0.0199 0.0133 0.0135 0.0139 0.0119wehoaling (0.0045) (0,0048) (0.0043) (0.0042) (0.0042) (0.0043)

Femiale iccondary -0.0071 -0.0162 -0.0080 -0.0102 -0.009s -0.0094schooling (O.OOSO) (0.0054) (0.0050) (0.0048) (0.0047) (0.0048)

Log (life 0.0793 0.0903 0.0829 0.0701 0.0781 0.0666expectancy) (0.0138) (0.0148) (0.01M7) (0.0157) (0.0148) (0.0160)

U/Y 0.081 0.073 0.079 0.063 0.07S 0.052(0.028) (0.027) (0.028) (0.028) (0.027) (0.029)

ClY -0.160 -0.145 -0.157 -0.160 -0.160 -0.151(0.035) (0.033) (0.034) (0.034) (0.034) (0.03S)

Log (1 + blAck- -0.0307 -0.0276 -0.0310 -0.0303 -0.0306 -o.ozaomarket premium) (0.0093) (0.0091) (0.0094) (0.0097) (0.0095) (0.0098)

Revolutions -0.0170 -0.0187 -0.0187 -0.01 68 -0.0178 -0.0164(0.0088) (0.0087) (0.0090) (0.0090) (0.0089) (0.0092)

Male higher -0.01Sschooling (0.02S)

Female higher -0.009schooling (0.032)

Growth of male 0.289secondary (0.121)schooling

Growth of female -0.453secondary (0.193)schooling

Male secondary 0.0072enrollment (0.0117)

Female secondary -0.0119enrollment (0.0162)

Log (IPERT) -0.0088 -0.0238(0.0064) tO.0118)

Growth rate of -0.090 0.57population (0.200) (0.38)

R2 (number of 0.56 (85) 0.58 (85) 0.56 (85) 0.54 (84) 0.56 (85) 0.S3 (84)observations) O.S7 (9S) 0.57 (9S) 0.56 (93) 0.S7 (9S) 0.56 (95) O.S7 (95)

Serial correlation 0.01 0.01 0.00 0.02 0.01 0.03

for 1965-75 and 1975-85, respectively. The estimated coefficient is signifi-cantly positive, 0.120 (s.e. = 0.020), as is typical of growth regressions. Thesize of the coefficient means: that a rise in II Y by 10 percentage points raises thegrowth rate by 1.2 percentage points a year. The mean of Il Y was 0.19 (s.d. =0.09) in 1965 and 0.20 (s.d. = 0.07) in 197S. Even if the decadal average of II Ywere regarded as exogenous with respect to the growth rate (see below), it isdifficult to use the estimated coefficient to infer a rate of return on capital. Someassumptions about depreciation are required for this calculation.

278 Losers and Winners in Economic Growtb

Table 5 (Part 3)

3SLS 3SLS 3SLS 3S1.5 3SLS 3SLSVariable (12) (13) (14) (IS) (16J (17)

Log (GDP) -0.0253 -0.0250 -0.0278 -0.0253 -0.02S7 -0.02O60(0.0034) (0.0035) (0.0051) (0.0035) (0.0035) (0.0038)

Male secondary 0.0112 0.0157 0.0113 0.012K 0.0139 0.0090schooiing (0.0042) (0.0040) (0.0048) (0.0042) (0.0042) (0.0044)

Female secondary -0.0072 -0.0100 -0.0088 -0.0089 -0.0094 -0.0052schooling (0.0047) (0.0045) (0.0054) (0.0047) (0.0047) (0.0047)

Log (life 0.0643 0.0685 0.1102 0.0798 0.0803 0.0712expectancy) (0.0148) (0.0145) (0.0217) (0.0139) (0.0140) (0.0148)

1/Y 0.082 0.076 0.054 0.077 0.078 0.078(0.026) (0.026) (0.043) (0.027) (0.028) (0.028)

GIY -0.155 -0.176 -0.167 -0.158 -0.155 -0.131(0.033) (0.033) (0.048) (0.034) (0.034) (0.037)

Log(1 + black- -0.0332 -0.0324 -0.0224 -0.0260 -0.0315 -0.0332market premium) (0.0091) (0.0098) (0.0174) (0.0099) (0.0094) (0.0087)

Revolutions -0.0132 -0.0190 -0.0182 -0.0159 -0.0182 -0.0163(0.0087) (0.0082) (0.0157) (0.0090) (0.0092) (0.0087)

Change in share -0.185of population (0.075)under age 15

Tariff rate -0.048(0.076)

Growth rate of 0.061tenns of trade (0.072)

WARDUM -0.0036(0.0033)

WARTIME 0.004(0.015)

Sub-Saharan -0.0116Africa (0.0051)

Latin America -0.0087(0.0037)

East Asia 0.0040(0.0057)

R2 (number of 0.59(85) 0.57(72) 0.49(40) 0.55 (85) 0.56 (85) 0.57 (85)observations) 0.56 (95) 0.64(80) 0.56(54) O.S7(95) 0.56 (95) 0.60 (95)

Serial correlation 0.01 0.00 0.00 0.01 0.01 0.01Note: Numbers in parentheses arc standard errors (except for the R2 row, where they denote number of

countries). The seemingly unrelated (SUR) technique estimates a system of two equations, with the dependent

variables observed for the decades 1965-75 and 1975-85. Each equation has a different constant term, which is

not shown in the table. The 3sLs (thrce-sag Icast squares) technique uses agged values of some of the

explanatory variables as instruments.

The variable GI Y is the average over each decade of the Summers and Heston(1988) ratio of real government consumption to real GDP less the ratio of nomi-nal spending on defense and noncapital expenditures on education to nominalGDP. (We do not have cleflators available for spending on defense and educa-tion.) Expenditures for defense and education wsre eliminated because these

Baro and Lee 279

outlays are not properly viewed as consumption; in particular, they are likely tohave direct effects on productivity or on the security of property rights. Theestimated coefficient of G/Y, -0.170 (s.e. = 0.026), is significantly negative.The mean of GI Y was 0.10 (s.d. = 0.06) in 1965-75 and 0.11 (s.d. = 0.06) in1975-85. Thus, a one-standard-deviation increase in GI Y is associated with adecline in the growth rate of 1.0 percentage point a year.

The variable log(i + BMp), where BMp is the black-market premium on for-eign exchange, is measured as an average for each decade. The estimated coeffi-cient is significantly negative, -0.028 (s.e. = 0.005). This variable takes on thevalue zero for some economies (twenty-four out of eighty-five for 1965-75 andtwenty-two out of ninety-five for 1975-85) and has an overall mean of 0.15(s.d. - 0.20) in the first decade and 0.23 (s.d. = 0.36) in the second. Thus aone-standard-deviation increase in the BMP variable in the first decade was esti-mated to reduce the growth rate by 0.6 percentage points a year.

The variable for revolutions is the average number of successful and unsuc-cessful revolutions a year over the full sample, 1960-85.7 We view this variableas representing the probability of revolution; in this sense it influences propertyrights and therefore affects the incentive to invest in various activities. Thevariable averaged over the full sample turned out to have more explanatorypower for growth than the average for each decade entered separately into eachdecadal equation. This result could indicate that the true probability of revolu-tion was roughly constant over time for an individual country; in this case, thelonger average would be better than the decadal figure as an estimate of theprobability in each decade.

The estimated coefficient of the revolution variable is significantly negative,-0.0171 (s.e. = 0.0082). For many economies the variable takes on the valuezero (twenty-seven out of eighty-five in the first decade and thirty out of ninety-five in the second). Overall, the mean of the variable was 0.15 (s.d. = 0.18).Thus a one-standard-deviation increase in the propensity for revolution wasassociated with a decline by 0.3 percentage points a year in the growth rate. It is,of course, likely that the probability of revolution responds to economicoutcomes,-that is, that the variable is endogenous to growth. We consider lateran instrumental estimate of this coefficient.

The regressions also include different constant terms for each decade. Onenotable result is that the excess of the constant for the first period over that ofthe second period is 0.014, with a t-value of 5.4. Thus, for given values of theexplanatory variables, the estimated growth rate for 1975-85 was lower thanthat for 1965-75 by 1.4 percentage points a year.8

Instrumental Estimates

The regression in column 2 of table S is the same as that in column 1 except thatlagged values of some of the explanatory variables are used as instruments. Theinstruments are the five-year lag of log(GDi;) (the 1960 value in the first decade

280 Loses and Winners in Economic Growth

and the 1970 value in the second) and the averages of I/ Y, GI Y, and log(1 +BMP) during the five years preceding each decade. The absence of serial correla-tion in the residuals of the growth equation suggests that these lagged variableswould be good instruments in the present context.

The use of an earlier value of log(GDP) as an instrument lessens the tendency tooverestimate the convergence effect because of measurement error in GDP. Theuse of a lagged value of I/ Y as an instrument would tend to lower the estimatedcoefficient on I/ Y if there is reverse causation from growth to investment oppor-tunities and hence to the investment ratio. The variable C / Y may be negatvelyrelated to contemporaneous growth from the mechanical effect whereby anincrease in Y lowers GI Y for given G. Wagner's law-the idea that governmentspending is a luxury good-would go the other way, but Wagner's law does notactually hold for government consumption as defined. (It holds well for transfersand educational spending.) The use of the lag of GI Y as an instrument shouldeliminate these problems. Finally, it is possible that the black-market premiumwould relate negatively to growth and thereby bias downward the estimatedcoefficient of the log(l + aMP) variable. The use of the lag of log(l + nBp) as aninstrument should correct this problem.

The system in column 2 of table S uses the starting values of school attainmentand lifc expectancy as their own instruments. This procedure seems satisfactorybecause these variables are predetermined. The revolution variable is also usedas its own instrument in column 2 (see below).

A comparison of columns 1 and 2 shows that the changes in coefficientestimates are minor, overall. The main change is a reduction in the estimated-coefficient of IIY from 0.120 (s.e. = 0.020) to 0.077 (s.e. = 0.027). Thus it islikely that the effect of investment on growth was overstated in the suRL regres-sion of column I because of reverse causation from growth to the propensity toinvest. For subsequent purposes we use the regressions that are based on instru-mental estimation.

Column 3 of the table uses the average number of revolutions a year in thepreceding five years (1960-64 for the first decade and 1970-74 for the second) asinstruments for the revolution variable. This change has litde effect on the results,induding the point estimate of the revolution coefficient, but does necessitate asignificant reduction in the sample size because of missing data on revolutions inthe early part of the sample (see note 7). Reverse causation would be important forrevolutions, but the linkage is likely to be more from the level of income to thepropensity to revolt than from the growth rate to this propensity. Therefore it isconceivable that economic adversity would promote revolutions, and yet theestimated coeffici2nt of the revolution variable would not be seriously biased in thespecification of column 2. To avoid the substantial falloff in the number ofobservations, we therefore return in the subsequent analysis to the specification inwhich the revolution variable is used as its own instrument.

Columns 4 and S of table 5 show the results when the countries with 1965 GDP

per capita below the median ($1,350 in 1980 prices) are separated from those

Barro and Lee 281

above the median. Some differences show up from a comparison of the twocolumns; for example, the coefficients for schooling and life expectancy vari-ables are larger for the poorer countries, whereas the reverse holds for theinvestment ratio and the black-market premium. The most striking observation,however, is the degree of similarity between the two sets of coefficients, despitethe great difference in average levels of real GDP per capita between the twogroups ($693 versus $3,788 in the first decade and $926; versus $5,087 in thesecond). For example, the estimated coefficient on initial 10g(GDP) is -0.0239(s.e. = 0.0078) for the poorer countries and -0.0255 (s.e. = 0.0052) for thericher countries. Thus, the estimated rates of conditional convergence are aboutthe same for the two groups. A likelihood-ratio test for the hypothesis that alleight coefficients are the same between the two groups is accepted at theremarkably high p-value of 0.96.

Columns 6 through 8 add some additional measures of educational attain-ment. Column 6 shows that the initial values of male and female attainment atthe higher level are each insignificant for growth. (The same holds for initialattainment at the primary level.) Theories that rely on discoveries of new kindsof goods as a driving force for technological progress, such as Romer (1990),predict a strong role for human capital in the form of higher education. It is notsurprising that this kind of basic innovation-the type of technological progressthat would most likely be linked to college education-would be unimportantfor most countries that tend to adopt leading technologies rather than inventnew ones. The higher-education variables are still insignificant, however, if thesampie is limited to countries with initial values of real GDP per capita above themedian or even to a group of twenty-one major industrial countries.

Column 7 adds the contemporaneous growth rate, over each decade, of maleand female secondary schooling.9 These variables, rather than the initial levelsof schooling that we discussed before, would appear in standard growthaccounting exercises. The exogeneity of the growth rates of attainment can bequestioned, although they are largely predetermined by prior years of schoolenrollment. In any event, the results in column 7 use the growth rates of attain-ment as their own instruments.

The estimated coefficient of the growth rate of male secondary schooling issignificantly positive, 0.29 (s.e. = 0.12), a notable achievement since someprevious growth-accounting exercises for a broad group of countries fail to findthis kind of positive effect (see, for example, Benhabib and Spiegel 1992). Bycontrast, the growth rate of female secondary atainment enters negatively,-0.45 (s.e. = 0.19). Note that the coefficients of the initial levels of secondaryattainment reemain positive for males and negative for females; in fact, thesecoefficents each rose in magnitude from the values shown in column 2. We donot have a convincing story to explain the apparently negative growth effectfrom an increase in female attainment.

Column 8 shows the results when male and female secondary school enroll-ment ratios are added to the basic regressions. These variables have been fre-

282 Losen and Winners in Economic Growth

quently used by previous researchers, who did not have access to data on stocksof school attainment. The main point is that the school enrollment ratios areinsignificant, whereas the coefficients of the measures of starting stocks ofattainment remain at about the same magnitude as the values shown in column 2.

Columns 9 through 11 enter measures of fertility and population growth,variables that have a negative effect on the steady-state level of output pereffective worker in the neoclassical growth model. This effect is strengthened inmodels that introduce time costs of having and raising children. Thus the usualview is that higher fertility and population growth would lower the growth rateof GDP per capita for given values of the explanatory variables that we havealready considered.

Column 9 includes the total fertility rate, the measure used by the UnitedNations to show the typical woman's prospective number of live births over herlifetime. The regression variable is the log of the average of fertility rates overeach decade and is used as its own instrument. The estimated coefficient isnegative but marginally insignificant. Column 10 adds the growth rate of popu-lation over each decade to the basic regressions. This variable is also entered asits own instrument. The estimated coefficient is negative but is less significantthan the fertility variable.

These results differ from those in some other studies because the life expec-tancy variable is already included in the equations. The log of life expectancy atthe start of each period is substantially negatively correlated with the log ofaverage fertility over the period (-0.83 in the first decade and -0.86 in thesecond) and with population growth (-0.63 in the first period and -0.75 in thesecond). If life expectancy is omitted from the regressions, the fertility variableand the growth rate of population have significandy negative coefficientestimates.

Column 11 includes simultaneously the fertility variable and the growth rateof population, with life expectancy also present in the equations. The estimatedcoefficient on the log of average fertility is now significantly negative, whereasthat on the population growth rate is positive and marginally insignificant. For agiven fertility rate, a higher population growth rate signals high net immigrationor low mortality-elements that would plausibly be positively related to growth.(Population growth would, however, also depend on age structure and the agesat which mothers typically have children.)

Column 12 indudes as an alternative demographic variable the change in theshare of the population that is under age 15. An increase in this share tends tolower the per capita growth rate, partly because of the increase in the number ofpersons of nonworking age and partly because the work effort of adults wouldbe directed more toward child-rearing. (These effects have been stressed by Sarel1992.) This population-share variable is significandy negative in the regressions.Also, when this variable is included, the ferility and population-growth vari-ables are insignificant, as is a variable that measures the change in the share ofthe population age 65 and over.

Barro and Lec 283

Lee (1992) estimated growth equations that induded a measure of tariff rateson capital goods and intermediate products. The tariff rate was interacted withan economy's natural openness, which depends on area and distance from othermarkets. The idea is that an economy that would naturally be more open-because it is small or close to other markets-suffers more when internationaltrade is distorted. Column 13 of table 5 includes Lee's tariff rate variable, whichis observed only for the single year 1980 for each country. The variable serves asits own instrument. The estimated coefficient is negative but insignificant. Thesample is also much smaller than before because of the limited availability of thetariff rate data.

It is frequently argued that countries, especially developing countries thatexport mainly primary products, are substantially affected by shocks to the termsof trade. The theoretical effects of changes in the terms of trade on GDP-as

opposed to the effects on real national income or consumption-are, however,ambiguous. For example, if a drop in the relative price of a country's principalexport leads to no change in physical production, GDP would not change(although the country would be worse off). GDP would fall if the countryresponded to the shock by lowering production, but it is conceivable that aworsening of the terms of trade would have the opposite impact. In any event, theeffects on GDP growth depend on the responses of domestic production to thechanged incentives implied by the shift in the terms of trade. One likely influence,for example, is that an increase in the relative price of oil-an import for mostcountries-would reduce the production of goods that use oil as an input.

We have computed the growth rate of the ratio of export prices (or export unitvalues) to import prices (or import unit values) over the two decades, 1965-75and 1975-85. The data, from the International Monetary Fund's InternationalFinancial Statistics (various years) are limited to about half the economies in thesample. This terms of trade variable is entered and used as its own instrument incolumn 14 of table 5. The estimated coefficient is positive but insignificant.(Note that the number of observations falls to forty in the first decade and fifty-four in the second.) We think, however, that the analysis of growth effects fromchanges in the terms of trade warrants further exploration with more and betterdata.

We have already discussed the growth effects of revolution, which we took asa measure of domestic political stability. Economies are also affected by warswith other countries. Our only data on external wars come from two measurespreviously constructed by Barro (1991): wARDUM, a dummy variable for coun-tries that participated in at least one external war over the period 1960-85, andWARTnME, an estimate of the fraction of time over 1960-85 that the country wasinvolved in an external war. Neither variable measures the severity of the wars,as reflected, for example, in expenditures, casualties, or destruction of property,or the outcomes. For the eighty-five economies in the 1965- 75 sample, WAR-

DUM has a mean of 0.39 and WARTIME a mean of 0.058. For the ninety-fiveeconomies in the 1975-85 sample, the means are 0.37 and 0.055, respectively.


Column 15 of table 5 includes the variable WARDUM, entered as its owninstrument. The estimated coefficient is negative but insignificant. Column 16includes the variable WARTIME, also entered as its own instrument. The esti-mated coefficient of this variable is roughly zero. W'e think, however, that ourfailure to find important growth effects from external wars involves the poorquality of our data rather than the unimportance of war.

Finally, column 17 includes regional dummy variables for Sub-SaharanAfrica, Latin America, and East Asia. For the economies included in the regres-sions, the average per capita growth rate from 1965 to 1985 in Sub-SaharanAfrica was 1.2 percentage points below that of the overall mean, whereas that inLatin America was 1.0 percentage point below the mean and that in East Asiawas 3.2 percentage points above the mean. Significant coefficients on the dum-mies indicate that the model does not adequately explain the systematic varia-tion in growth rates across these regions.

The estimated coefficients of the Africa and Latin America dummies are sig-nificantly negative, -0.0116 (s.e. = .0051) and -0.0087 (s.e. = .0037),respectively. The estimated coefficient of the East Asia dummy is positive butinsignificant: 0.0040 (s.e. = .0057). Thus although the Africa and Latin Amer-ica dummies are smaller and less significant than in some previous research, suchas Barro (1991), the model still does not fully explain why the countries in Sub-Saharan Africa and Latin America experienced below-average growth rates. Wereturn to this issue in the next section. The model does capture the high growthrecorded in East Asia.

Sources of Growth for the Slow and Fast GrowersThe basic equation in column 2 of table 5 is the source of the fitted values for1965-75 and 1975-85 for the slow and fast growers that are shown in tables 3and 4. The projected growth rates for 1985-9S come from the same estimatedmodel, where the values of the explanatory variables are those applying in 1985for log(GDP) and secondary schooling and as averages for 1980-84 for GIY,I/ Y, and log(life expectancy). The revolution variable takes on the same value asin the regression samples. We have already noted that the fitted values for 1965-75 and 1975-85 explain a substantial part of the observed differences in percapita growth rates between the slow and fast growers. Therefore, although theremaining residual errors in individual country growth rates are also substantial,it is worthwhile to examine the differences in the explanatory variables thatgenerate the differences in the fitted growth rates.

We can use the regression results to break down the fitted and projectedvalues of growth rates for each country into the contributions from each of theeight explanatory variables that appear in the basic model shown. This pro-cedure provides a form of growth accounting in which the determining variablesare, unlike the growth rates of factor inputs, arguably exogenous influences.One conclusion from this exercise is that the fitted growth rates depend on the

Barm and Lee 285

t'J00

Table 6. Sources of Growth, by Economy Group

Group and number Convergence Black-market Fitted Aetualof economies Period effect (net) I/Y G/Y premium Revolution growth growth

Slow-growing

Sub-Saharan Africa (14) 196S-75 -0.002 -0.006 -0.011 -0.002 -0.003 -0.023 -0.0281975-85 0.006 -0.006 -0.010 -0.009 -0.003 -0.022 -0.0391985-9S 0.017 -0.007 -0.011 -0.007 -0.003 -0.010 -

Latin America (5) 196S-75 -0.009 0.000 0.002 -0.003 -0.001 -0.011 -0.0191975-85 -0.004 -0.003 -0.008 -0.010 -0.001 -0.025 -0.0411985-95 0.002 -0.003 -0.011 -0.016 -0.001 -0.029

Fast-growingSub-Saharan Africa (4) 1965-75 0.022 -0.004 -0.006 0.000 0.002 0.014 0.032

1975-8S 0.010 0.000 -0.008 0.001 0.002 0.004 0.016198S-9s 0.004 0.001 -0.008 0.003 0.002 0.001 -

Latin America (3) 1965-75 0.004 0.002 0.002 0.000 0.000 0.010 0.0261975-85 -0.005 0.002 0.002 0.001 0.000 0.000 0.0121985-95 -0.007 0.002 0.003 0.000 0.000 -0.002 -

v EastAsia(8) 1965-75 0.016 0.002 0.006 0.003 0.001 0.028 0.031I'197S-8S 0.008 0.006 0.010 0.006 0.001 0.030 0.042

1985-95 -0.001 0.006 0.010 0.007 0.001 0.023 -OECW members (6) 1965-75 0.005 0.008 0.001 0.004 0.001 0.020 0.016

1975-85 0.005 0.005 0.001 0.005 0.001 0.017 0.02S1985-95 -0.005 0.005 0.001 0.007 0.001 0.008

S' - Not available.W, Note: Each entry shows the average contribution of the indicated variable to the fitted growth rate of real cDP per capita (expressed in relation to the sample mean). The

contributions are averages for the designated group of economies and time period. The net convergence effect is the combined impact from the initial values of log(GDP), male and. female secondary school attainment, and log(life expectancy). The fitted growth rate is the sum of the contributions shown separately. The actual growth rate refers to the average

C deviation from the sample mean for of the indicated group of countries and time period.d_

combined influence of several factors, rather than on one or two key elements.To bring out some general tendencies, however, we combine the results intoregional groups of slow- or fast-growing countries in table 6. The contributionsof the explanatory variables to the fitted growth rate are averaged for sixgroups. For the slow growers, we examine fourteen Sub-Saharan African econ-omies and five Latin American economies. For the fast growers, we considerfour Sub-Saharan-African economies, three Latin American economies, eightEast Asian economies and six members of the Organization for Economic Coop-eration and Development (OECD).

To ease the presentation, the table combines the contributions from the initialvalues of lOg(GDP)9, male and female secondary schooling, and life expectancy intoa net convergence effect. That is, this variable shows the contribution to the fittedgrowth rate (as a deviation from the sample mean) for initial nDP per capita, whenconditioned on the initial values of human capital per person. The table showsseparately the contributions to the fitted growth rate from the investment ratio,I/ Y; the government consumption ratio, G/ Y; the black-market-premium vari-able; and the revolution variable. The sum of the individual contributions givesthe fitted growth rate as a deviation from the sample mean, as shown in the nextto last column of the table. The final column shows the actual average growth ratefor the group, also as a difference from the sample mean.

For the fourteen slow-growing Sub-Saharan African countries in the period1965-75, the net convergence effect is dose to zero; that is, the positive effect ongrowth from low initial income is roughly canceled, on average, by the negativeeffects from low secondary school attainment and low life expectancy. Thenegative value for fitted growth of -0.023 then reflects the contributions fromlow investment (-0.006), high government consumption (-0.011), moderatedistortions as reflected in the black-market premium variable (-0.002), and anadverse effect from political instability as represented by the revolution variable(-0.003). The average of the actual growth performance, -0.028, is somewhatworse than that indicated by the fitted value.

In the 1975-8S decade, the net convergence term switches to positive territory(0.006), basically because levels of GDP per capita fell in the previous decade inrelation to secondary attainment and life expectancy. The negative contribu-tions from I/ Y and G/ Y are about the same as in the previous period (and thecontribution from revolutions is the same by construction), but the black-market premium term becomes more adverse (-0.009). This change presuma-bly reflects an increase in governmental distortions, possibly triggered byadverse movements in the terms of trade (which we have not held constant). Inany event, the fitted growth-rate term is now -0.022, which is well above theactual value of -0.039, in relation to sample means.

This failure to explain the extent of the poor growth performance in the slow-growing African countries in 1975-85 is the source of the significance of theAfrica dummy variable in the regressions discussed before.'0 A likely reason forthe underestimate of the extent of the adversity is that the variables induded to

Barro and Lee 287

measure governmental distortions and political instability-ClY, the black-market premium, and revolutions-understate these difficulties in Africa. Abetter measure of terms of trade shocks-entering partly as a direct effect ongrowth and partly as a stimulus to bad government policies-might also help inthis context.

The clearest contrast for the group of fourteen slow-growing Sub-SaharanAfrican countries is the group of eight fast-growing East Asian economies. Table6 shows that the contribution from the net convergence term is substantiallypositive (0.016) for the East Asian group in 1965-75. In other words, the initiallevels of real GDP per capita were low, on average, in relation to the levels ofsecondary attainment and life expectancy. The other four terms are also posi-tive: 0.002 for I/ Y, 0.006 for Cl Y, 0.003 for the black-market premium, and0.001 for revolutions. There were favorable growth effects from moderatelyhigh investment, markedly low government consumption, a lack of distortions(as indicated by a low or zero black-market premium), and the presence ofpolitical stability (as reflected in a low propensity to revolt). These factors,therefore, all operate in a direction opposite to that in the slow-growing Africancountries. Overall, the fitted growth rate for the eight East Asian fast growers in1965-75 is 0.028, compared with the actual value of 0.031.

For 1975-85 the contribution from the net convergence term for the EastAsian economies falls to 0.008 because CDP rose in relation to the levels ofsecondary attainment and life expectancy. Three of the other terms becomemore favorable, however. The contributions are now 0.006 from I/ Y, 0.010from C/ Y, and 0.006 from the black-market premium. That is, in 1975-85 theEast Asian economies enjoyed even greater positive contributions to growthfrom high investment, low government consumption, and absence of distor-tions, as reflected in a low or zero black-market premium. The overall fittedgrowth rate of 0.030 is below the actual value of 0.042 (see note 10).

Finally, the projected growth rates for the eight East Asian economies in19 85-95 continue the previous trend: the net convergence effect becomessmaller (-0.001) but the contributions of the other terms are maintained orenhanced. Consequently the projected growth rate for 1985-9S, in relation tothe sample mean, is still the high value of 0.023.

Another natural comparison is between the group of fourteen slow-growingSub-Saharan African countries and the group of four fast-growing Sub-Saharan African countries. For 196S-75 the fourteen African slow growersdiffer from the four fast growers most clearly in the net convergence term,which is -0.002 for the former group and 0.022 for the latter. That is, the fastgrowers have particularly low values of initial GDP in relation to their levels ofsecondary schooling and life expectancy. The fast growers also get bettercontributions from IIY (-0.004 versus -0.006), GIY (-0.006 versus-0.011), the black-market premium (0.000 versus -0.002), and revolutions(0.002 versus -0.003). In other words, the fast growers have less tendencytoward big governments, distortions, and political instability and have some-


what higher investment ratios. Basically similar conclusions apply for the1975-85 period.

Two of the African fast growers, Botswana and Lesotho, are neighbors orenclaves of South Africa, and this could provide spillover benefits, such as readyaccess to capital and skilled managers that would spur economic growth. Chua(1993) has made this point and has provided some empirical support for itsimportance. This idea could explain, for example, why the average residual forBotswana for 1965-85 is 0.034 and that for Lesotho is 0.017.11

A possible counterclaim is that other neighbors of South Africa, such asMozambique, have not performed well.'2 Mozambique is on the slow-growerslist, with growth rates in relation to the sample means of -0.038 in 1965-75and -0.053 in 1975-85. The remarkable thing, however, is that the residualsfor Mozambique are strongly positive: 0.021 and 0.032, respectively. For exam-ple, in 196S-75 the contributions to the fitted growth rate are -0.024 for netconvergence, -0.009 for I/Y, -0.007 for G/IY (estimated from the meanbehavior for Sub-Saharan Africa because of missing data), -0.004 for theblack-market premium, and -0.015 for revolutions. Thus a possible interpreta-tion is that, if not for the proximity of South Africa, in 1965-75 Mozambiquemight have grown at 6 percentage points a year below the mean rate of 2.9percent a year instead of only 4 percentage points below. Similarly, in 1975-85it might have grown at 8 percentage points a year below the mean rate of 1.3percent a year instead of only 5 percentage points below.

Table 6 also allows a comparison between five slow-growing and three fast-growing Latin American countries. (Two of the fast growers, Brazil and Ecu-ador, had notably high growth rates only in the 1965-75 period.) For 1965-75the main differences are the greater contributions from the net convergence termand the black-market premium for the fast growers. In 1975-85 the net conver-gence effects are similar for the two groups, but the fast growers do better interms of higher investment, substantially smaller government consumption, andmuch lower distortions (as proxied by the black-market premium). For 1985-95the net convergence term for the slow growers is larger than that for the fastgrowers: 0.002 versus -0.007. Nevertheless, the inferior results on the othervariables yields an average projected growth for the slow growers, in relation tothe sample mean, of -0.029, compared with -0.002 for the fast growers.

Especially noteworthy are the contributions from the black-market premiumfor the slow growers of -0.010 in 1975-85 and -0.016 in 1985-95. Theseeffects proxy for a remarkable degree of market distortion in the slow-growingLatin American countries. The significance of the Latin American dummy in thegrowth rate regressions probably reflects the failure of the explanatory variablesthat we have been able to measure to capture fuily the extent of the marketdistortions in this region.

Finally, table 6 indudes the group of six fast-growing OECD countries. In 1965-75 the net convergence effect is positive (0.005) because the relatively high levelsof initial GDP per capita are more than offset by the relatively high values of

Bamo and Lee 289

secondary attainment and life expectancy. The other main positive contributionsto growth are from high investment (0.008) and low distortions, as reflected inlow or zero black-market premiums (0.004). Overall, the fitted growth rate is0.020 above the sample mean, compared with an actual value of 0.016.

For 1975-8S the net convergence term remains at 0.005. The contributionfrom investment declines, but that from low distortions (small or zero black-market premiums) rises slightly, to 0.005. Overall, the fitted growth rate is now0.017 above the sample mean, compared with an actual value of 0.025. For1985-95 the rise in GDP in relation to schooling and life expectancy reduces thenet convergence term to -0.005. This change lowers the average projectedgrowth rate for the six OECD countries to 0.008 above the sample mean.

Determinants of Fertility and Health

The results presented thus far are somewhat disappointing in terms of demon-strating an important role for educational attainment in the growth process.Secondary education of males has a significantly positive effect on growth rates,but that of females has a puzzling negative effect. Primary and higher levels ofeducation do not have significant power in explaining growth.

This section provides a preliminary analysis of the influences of educationalattainment on the quantity and quality of children, where quantity is measuredby the fertility rate and quality by two health indicators, infant mortality and lifeexpectancy at birth. A broader analysis would extend the concept of childquality to include schooling, but we do not yet provide an analysis of thelinkages between parental and child education. (In our aggregate data, this effectwould show up as a connection between school attainment and subsequent ratesof school enrollment.)

Our results reveal strong relationships of fertility and health to school attain-ment, especially of females. These relationships suggest indirect channels thatwould strengthen the ultimate effect of schooling on growth; that is, moreeducation, particularly of females, seems to lead to lower fertility, better health,and probably to improved education of children, and these responses wouldraise the rate of economic growth in the long run. Previous discussions of thesekinds of linkages appear in Behrman (1990), Schultz (1989), and Bhalla and Gill(1992).

Fertility

The first three columns of table 7 report regressions for the ferdlity rate. Themodel is a system of three equations, with the variables observed in 1965, 1975,and 1985. Estimation is by the seemingly unrelated (suR) technique; that is, thepanel estimation allows each country to have random effects that are correlatedover time.

290 Losers and Winne in Economic Growtb

The dependent variable is the log of the total fertility rate, observed in 1965,1975, and 1985. In 1965 the mean of the dependent variable was 1.60, corre-sponding to a fertility rate of S.0, and in 1985 the mean was 1.31, or a fertilityrate of 3.7.

The first column of the table uses real GDP per capita (from Summers andHeston 1988) and total years of female and male school attainment (from Burroand Lee 1993) as independent variables. (These variables are also observed in1965, 1975, and 1985.) The specification includes a linear and a squared termfor each regressor; that is, log(fertility) is allowed to respond nonlinearly to thecontemporaneous values of incomc and schooling. The fits improve somewhat iflagged values of income and schooling are also entered into each regression, butthe general nature of the results does not change.

The terms in brackets in the table indicate the p-values for the joint signifi-cance of the linear and squared terms for each of the three independent vari-ables. Therefore, the first conclusion from the regressions is that fertilitydepends significantly on GDP per capita and female school attainment (bothp-values = 0.00), but not on male school attainment (p-value - 0.16). Thus,for given income, the schooling of the mother, but not of the father, appears tobe the key determinant of the number of children. This result is consistent withthe idea that, especially in developing countn"es, children absorb primarily thetime and energy of the mother.

For income, the linear term of 0.56 (s.e. = 0.24) and the squared term of-0.047 (s.e. = 0.016) are each significantly different from zero. This configu-ration of coefficients means that fertility initially rises with income but subse-quently falls. The implied breakpoint is, however, at a real GDP per capita ofonly S400 a year (in 1980 U.S. dollars). In 1965 only 6 percent of the includedcountries had per capita product below this level. Thus at very low incomes-observed for only a few countries-the Malthusian effect dominates, and moreincome leads to more children. For the overwhelming majority of countries, theeffect of higher income on fertility is negative. This relation can reflect theincreased value of time of parents (for given levels of educational attainment), asubstitution of quality of children for quantity as income rises, and increasedknowledge about birth control.

The estimated effect of female years of schooling involves the linear term,-0.157 (s.e. = 0.034), and the squared term, 0.0054 (s.e. = 0.0033). Since theestimated coefficient on the squared term is not significantly different from zeroat conventional significance levels, a linear form woultd be a satisfactory approx-imation. In any event, the implied breakpoint between negative and positiveeffects is 14.5 years of female schooling, a figure never attained by any countryin the sample. Therefore the estimated effect of female education on fertility isalways negative but it may decrease in magnitude as the level of schooling rises.

For male schooling the overall effect is statistically insignificant. The patternof point estimates, 0.006 on the linear term and -0.005 on the squared tern,implies that the transition from a positive to a negative effect occurs at 6.9 years

Barno and Lee 291

Table 7. Regressionsfor Fertilityand Healtb

Depeidew miaeIndependent Iog(FERT) Iag(FERT) JgFERT) MfOAT 5o57IHE,)variable (1) (2) (3) (4) (5Mean

1965 1.60 1.60 1.60 0.090 4.021985 1.31 1.31 1.31 0.061 4.13

LOglGDP 0.56 [0.00° 0.48 [0.041 0.44 [0.03) -0.0432 [0.001 0.171 [0.00)(0.24) (0.22) (0.22) (00152) (0.056)

Log(Gnv) squared -0.047 -0.035 -0.033 0.0CI83 -0.0071(0.016) (0.015) (0.015) (0.00101) (0.0037)

Female schooling -0.157 [0.001 -0.070 (0.02) -0.069 [0.021 -0.0064 10.001 0.0216 [0.00](0.034) (0.032) (0.031) (0.0022) (0.AW8)

Female schooling squared 0.0054 0.0026 0.0030 0.00003 0.00010(0.0033) (0.0030) (0.0030) (0.00020) (O.Ow75)

Male schooling 0.066 10.16) 0.050 [0-32) 0.059 10.181 -0.0147 [0.00] 0.051 [.OOD(0.033) (0.032) (0.032) (0.0022) (o.w82)

Male schooling squared -0.0048 -0.0038 -0.0044 0.00113 -O.0434(0.0029) (0.0028) (0.0028) (0.00019) (0.00069)

Log(LaFE) 26.7 10.00) 15.8 10.00)(3.1) (5.0)

Log(LIFE) squared -3.48 -2.06(0.39) (0.64)

2 lORT 6.1 10.021Li (21)

m MORT squared -23.7(9.0)

u RZ (number of observations)1965 0.68 (83) 0.80 (83) 0.81 (83) 0.75 (86) 0.79 (86

Z 1975 0.80 (91) 0.87 (91) 0.88 (91) 0.80 (94) 0.4 (91985 0.80 (91) 0.87 (91) 0.88 (91) 0.84 (94) 0.87 (93)

* Serial correlation 0.64 0.53 0.51 0.79 0.79Note. Standard erros ae in parenthese. The numbers in brackets sow p-iAn for mine siidn0 the iea varas b d sksr squad rAhes Earh m sm

estimation of a system of thrm equations, with the dependent variabl observed for 1965, 1975, and 1911. Cams E efawchpbeiodam sbn. mr sihemi kty8 rate; MORT is the infant mortalityratn, Un is the life xpecanc at birth; oGDP) is the log of eal crprcput stmfe -hh is 1e data d aS females; male schooling is the average years of attainment for adult maks. Serial cor_b2ioni5 dava fir*order vejhu sri cmvkirm m b%&S by hed eehn_W

residual correlation matrix.

of schooling. The fraction of countries with male attainment below this levelwas 87 percent in 1965 and 74 percent in 1985. Thus there is a weak indicationthat more male schooling-for given levels of income and female schooling-leads to increased fertility for the majority of countries but to reduced fertilityfor the countries with the most schooling. (A positive linkage could result ifmore male schooling has primarily an income effect because males in developingcountries typically spend a small fraction of their time in child-rearing.)

The fit of the regressions can be gauged by the R2 values for each of the threetime periods: 0.68 for 1965 (eighty-three economies) and 0.80 for 1975 and1985 (each with ninety-one economies). The residual errors are, not sur-prisingly, highly correlated over time for a given economy. The first-order serialcorrelation coefficient for the residuals is 0.64.

Fertility choice would also depend on life expectancy and infant mortality.Greater life expectancy raises fertility by increasing the survival rate of mothersand by making having children more attractive. But higher life expectancy alsomeans that a smaller number of births is required to generate a given number ofchildren who survive to adulthood. Similarly, a higher infant mortalitylratemakes child creation more costly-which deters fertility-but also raises thenumber of births required to achieve a given number of survivors.

Column 2 of table 7 adds the log of life expectancy at birth to the regressions,and column 3 also includes the infant mortality rate. These variables are enteredwith linear and squared terms. The two new variables are each statisticallysignificant; the p-values for joint significance of the linear and squared terms incolumn 3 are 0.00 for life expectancy and 0.02 for infant mortality.

For life expectancy the estimated coefficients in column 3 (1S.8 [s.e. = 5.01on the linear term and -2.06 [s.e. = 0.641 on the squared term) imply that theeffect on fertility is positive at low life expectancy but becomes negative whenlife expectancy exceeds 46 years. The fractions of economies with life expec-tancy below this number were 28 percent in 1965, 14 percent in 1975, and 4percent in 1985. Thus the typical pattern is for higher life expectancy to beassociated with lower fertility.

For the infant mortality rate the estimated coefficients in column 3 are 6.1(s.c. = 2.1) on the linear term and -23.7 (s.e. = 9.0) on the squared term. Theimplication is that higher mortality is associated with higher fertility if the mortal-ity rate is less than 12.8 percent-a condition that holds for 63 percent of thesample in 1965, 78 percent in 1975, and 91 percent in 1985. Thus in recent yearslower infant mortality-aike higher life expectancy-goes along with lowerfertility.

Infant Mortality and Life Expectancy

Columns 4 and 5 of table 7 view infant mortality and life expectancy as endo-genous variables to be determined by income and education. For example,higher income would be expected to lead to improved nutrition, sanitation, and

Barno and Lee 293

health care and would thereby tend to reduce infant mortality and raise lifeexpectancy. Similarly, greater education of parents ought to improve the healthoutcomes of children. Our initial expectation was that this linkage would begreater for female than for male education, again because mothers are morelikely to be involved with child-rearing. An offseLting force, however, is thatgreater educational attainment may motivate parents, especially females, to shiftattention away from children and toward market activities, a response thatcould weaken the positive relation between schooling and child health.

The estimates in column 4 imply that infant mortality is significandy nega-tively related to income and to female schooling; the nonlinear effects are notimportant here. For male schooling the effects are significant but involve apattern that switches from negative to positive when male schooling reaches 6.5years. The fraction of countries below the critical point was 84 percent in 1965,79 percent in 1975, and 68 percent in 1985. It is unclear what effect is picked upby the positive relation between infant mortality and male education fbr thecountries with high school attainment.

The results for life expectancy at birth in column 5 are similar to those forinfant mortality. Income and female schooling show up as significantly positiveinfluences on life expectancy. Male schooling switches from positive to negativewhen schooling reaches 6.3 years--that is, about the same point at which theswitch occurs for infant mortality. It is again puzzling that male educationwould be negatively related to life expectancy for countries with high levels ofschooling.

Conclusion

Differences in growth rates across economies are large and relate systematicallyto a set of quantifiable explanatory variables. One element of this set is a netconvergence term, the positive cffect on growth when initial real c.DP per capitais low in relation to the starting levels of secondary school attairment and lifeexpecctancy. Growth depends negatively on a group of variables that reflectdistortions and the size of government: the rato of govermnent consumption toGDP, the black-market premium on foreign exchange, and the frequency ofrevolutions. Growth depends positively on the ratio of gross investment to GDP

but not as strongly as in some previous studies.This set of explanatory variables discriminated reasonably well between the

economies that grew slowly on average from 1965 to 1985-for example, thetwenty-three economies in the lowest quintile of growth rates-and those thatgrew quickly-such as the twenty-three economies in the highest quintile.Although the tendency to grow slowly or quickly attenuated over time, therewas enough persistence so that the projected growth rates for 198S-9S had acorrelation of more than 0.4 with the actual growth rates for 196S-8S.

Successful positive analysis of economic performance is a prerequisite for thedesign of policies that would improve a country's well-being. Thus an important


objective is to use our results on the delerminants of economic growth to con-struct useful policy recommendations, especially for the slow-growing develop-ing countries that were the focus of much of our discussion.

Many economists jump readily from regression results to policy proposals,although valid inferences of this type are difficult to make. For example, theobservation that investment ratios and growth rates are positively related-evenwhen lagged investment ratios are employed as instruments in the growth-rateregressions-does not imply that investment has supernormal returns whichwarrant government subsidies or additional public projects. Similarly, the posi-tive effec.ts on growth from initial human capital in the forms of educationalattainment and health do not necessarily mean that governments are underin-vesting in education and health. We think that the safest policy implications thatcan be drawn at this point from our results involve the harmful effects on growthfrom distortions of markets (represented in the regressions by the black-market-premium variable) and from excessive government spending on consumptionitems. The results also support the idea that political instability is harmful forgrowth, although the policy implications are unclear because we do not provideinstruction on how governments could enhance politicaI stability.

Secondary school education plays a significant role in the growth regressionsbut a less important one than life expectancy. Our results also show a puzzlingnegative relation between growth and female education. Preliminary researchshows more important influences of schooling on choices of the quantity andquality of children-effects that should influence growth in the long run. Inparticular, female education has a marked negative relation with ferdlity andinfant mortality and a strong positive relation with life expectancy.

We plan to investigate further the role of education, especiafly of women, inthe determination of fertility and population growth, health, and education ofchildren. Then we shall consider how these channels relate to economic growthand, in particular, to the behavior of growth rates that we isolated in this paper.

Appendix. Economies Included in Growth-Rate Regressions

An asterisk (*) denotes an economy induded in the growth rate regressions for1975-85 but not for 1965-75.

Algeria Cameroon Egypt* HondurasArgentina Canada El Salvador Hong KongAustralia Cenrral African Rcp. Finland IndiaAustria Chile France IndonesiaBangladesh Colombia Gambia, Thc IranBarbados' Congo Germany IraqBelgium Costa Rica Ghana IrclandBenin Cyprus* Greee IsradBolivia Denmark Guatemala ltalyBotswana Dominican Rep. Guyana* JamaicaBrazil Ecuador Haiti Japan

(continued)

Barno and Lce 295

Jordan New Zealand Singapore Trinidad and Tobago*Kenya Nicarugua South Africa TunisiaKorea, Rep. of Niger Spain TurkeyLesotho Norway Sri Lanka UgandaLiberia Pakistan Sudan United StatesMalawi Panama Swaziland' United KingdomMalaysia Paraguay Sweden UruguayMali Peru Switzerland VenezuelaMalta Philippines Syria Yemen, Arab Rep. of'Mauritius' Portugal Taiwan (China) ZaireMexico Rwanda Tanzania ZambiaNepal Senegal Thailand ZimbabweNetherlands Sierra Leone' Togo

Notes1. We use data from Summers and Heston 1988 rather than 1991 because we were already set up with

these figures and because a comparison with regressions that used Summers and Heston 1991 showedlittle change in the results.

2. Easterly and others (1993) argue that growth rates for individual countries do not persist very muchover time. For the eighty-five economics in our growth regressions, the correlation of the growth rate for1965-75 with that for 1975-85 is 0.44. The correlation of the projected value for 198S-9S with thegrowth rate for 1965-8S is 0.42.

3. Kuwait has been omitted from the regressions because its real GDP per capita makes it too unusual.The regression results are, however, insensitive to the inclusion of Kuwait in the sample.

4. Note that the omitted variables, denoted by ... in the equation, dtecrmine the steady-state level ofoutput per "effectivew worker in the neoclassical growth model of a closed economny. A change in any ofthese variables, such as the saving rate, affects the growth rate for given values of the state variables. Butwe assume for now that these other variables are held constant.

5. This restriction, although dictated by the available data, is unfortunate because much of the laborforce in developing countries consist of younger persons. The main error, in comparison with, say, theattainment of the population age 15 and over, would be in the timing of changes in years of schooling forcountries that are experiencing large changes in school enrollment ratios.

6. The formula for the convergence coefficient g is (1 - e-fl-$lT = 0.0264, where T = 10 years is theobservation interval and 0.0264 is the magnitude of the estimated coefficient; see Barro and Sala-i-Martin(1952). This formula implies S = 0.031 a year.

7. The data, described in Banks (1979), cover the period 1960-85 for most countries. If a country'sobsCL vations are missing for part of the period (typically for the early years), we used the average of thenumbers for the years that werc available.

8. The mean growth rate for each deade depends also on the mean values of the regressors. For theeighty-five economics that were induded in the regressions for both decades, the average growth rate inthe first period exceeded that in the second period by 1.7 percentage points a year.

9. The variable for the first decade is 0.1 log[(1 + secondary attainment in 1975)/1( + secondaryarcainment) in 1965)], and analogously for the second decade. The inclusion of the I avoids problemswith very low levels of secondary attainment. The specification means that individuals are effectivelyendowed with skills equivalent to one year of secondary schooling before they start secondary school.

10. It is not surprising that economies selected for low (or high) growth rates tend also to have negative(or positive) residuals, on average. The observations about the slow-rowing Sub-Saharan African coun-tries stil hold qualitatively, however, if we consider all of Sub-Saharan Africa as a group.

11. A counterargument is that Botswana did well because of its natural resources, especially diamonds,and, similarly, that Gabon grew rapidly (until 1986) because of its oil. Natural resources do not appear,however, to be a key determinant of economic growth in a broad sample of countries. In particular, ifthese resources were the key to growth, the relatively poor performances of Nigeria and Zaire would be

296 Losers and Winners in Economic Grosnb

difficult to cxplain. (Nigeria is not in the regression samples because of the lack of census data oneducational attainment.)

12. Another enclave of South Africa, Swaziland, has a positive residual of 0.014 in the growthregression for 1975-85, but because ol missing data Swaziland was not included in the regression for1965-75. Another neighbor, Zimbabwe, has a residual of 0.004 for 1965-7S, but -0.004 for 1975-85.

References

Banks, A. S. 1979. "Cross-National Time-Series Data Archive." Center for Social Analysis, StateUniversity of Ncw York, Binghamton.

Barro, Robert J. 1991. "Economic Growth in a Cross Section of Countries." Quarterly Journal ofEconomics 106 (May): 407-43.

Barro, Robert J., andj. W. Lee. 1993. "International Comparisons of Educational Attainment.' HarvardUniversity, Economics Department, Cambridge, Mass.

Barro, Robert J., and X. Sala-i-Martin. 1992. 'Convergence." Journal of Political Economy 100 (April):223-51.

B2rro, Robert J., and X. Sala-i-Martin. 1993. "Economic Growth." Harvard University, EconomicsDepartment, Cambridge, Mass.

Behrman, J. R. 1990. "Women's Schooling and Nonmarket Productivity: A Survey and a Reappraisal."University of Pennsylvania, Economics Deparnment, Philadelphia.

Benhabib, Jesse, and M. M. Spiegel. 1992. "The Role of Human Capital and Political Instability inEconomic Development." New York University, Economics Department, New York.

Bhalla, S. S., and 1. S. Gill. 1992. 'Social Expenditure Policies and Welfare Achievement in DevelopingCountries." State University of New York, Economics Departnent, Buffalo.

Cass, David. 1965. "Optimnum Growth in an Aggregative Model of Capital Accumulation." Review of&onomic Studies 32 (July): 233-40.

Chua, H. B. 1993. "Regional Spillovers and Economic Growth." Harvard University, EconomicsDepartment, Cambridge, Mass.

Easterly, William R., Michael Krener, Las Pritchert, and Lawrence Summers. 1992. "Good Policy orGood Luck? Country Growth Performance and Temporary Shocks." World Bank, Country EconomicsDepartment, Washington, D.C.

Koopmans, T. C. 1965. "On the Concept of Optimal Economic Growth." In Pontifical Academy ofScicnces, The Econometric Approach to Development Planning. Amsterdam: North-Holland.

IMF (International Monetary Fund). Various years. International Financial Statistics. Washington, D.C.

Lee, J. W. 1992. "International Trade, Distortions and Long-Run Economic Growth." InternarionalMonetary Fund, Research Department, Washington, D.C.

Romer, P. M. 1990. "Endogenous Technological Change." Journal of Po&ltical Economy 98, pt. 2(October): 571-5102.

Sarel, Michael. 1992. 'Demographic Dynamics and the Empirics of Economic Growth:' HarvardUniversity, Economics Department, Cambridge, Mass.

Schultz, T. P. 1989. "Returns to Women's Education." PHRWD Background Paper 891001. World Bank,Population, Health, and Nutrition Department, Washington, D.C.

Solovw, Robert M. 1956. "A Contribution to the Theory of Economic Growth." Quarterly Journal ofEconormics 70 (February): 65-94.

Summers, Robert, and Alan Heston. 1988. "A New Set of International Comparisons of Real Productand Price Levels: Estimates for 130 Countries." Review of Income and Wealth 34: 1-25.

-. 1991. "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988." QuarterlyJounral of Economics 106 (May): 327-68.

Barro and Lee 297

COMMENT ON "'LOSERS AND WINNERS IN ECONOMIC GRowTH,"BY BARRO Am LEE

Gillian Hart

ince this is a session on the economics of regress, my comments will focusS on what Barro and Lee's paper does and does not tell us about the reasonsfor poor economic performance. Barro and Lee concede that their model

"does not ully explain why the countries in Sub-Saharan Africa and LatinAmerica experienced below-average growth rates." In fact, in the case of thefourteen slow-growing countries of Sub-Saharan Africa, the explanatory powerof the model declines quite dramatically over time. In the 1965-75 period, theactual growth performance of these countries was 22 nercent below the fittedvalue; by 1975-85, it had fallen to 77 percent below the fitted value. Thereasons for this growing disparity illuminate the phenomenon of regress.

I would like to address three aspects of the model: external factors, primarilywar and terms of trade (although drought and foreign aid are also important);political instability; and marker distortions and government spending.

War and Terms of Trade

The omission of external variables in the model is dhe first and most obviousreason for the growing gap between actual and predicted growth performance.In some of the regressions reported in table 5 in Barro and Lees paper, theauthors do include variables for war and tenns of trade, but they find theminsignificant and omit them from subsequent analysis. The authors do, ofcourse, recognize the importance of these variables, and they note that theirinsignificance in the regression is probably attributable to measurement prob-lems. Nevertheless, their analysis of growth performance is based entirely oninternal factors.

In the case of Sub-Saharan Africa this failure is problematic. Wheeler's (1984)regression analysis of the sources of stagnation in Sub-Saharan Africa suggests

Gillian Hart is associate professor of city and regional planning at the University of California, Berkeley.

Proceedings of the World Bank Annual Conference on Development Econmics 1993e 1994 The International Bank for Reconstruction and Development / THE wORw Baw 299

that what he termed "environmental" variables (diMate, violence or war, termsof trade, and foreign aid), taken as a set, were more important than policyvariables in explaining poor performance from 1970 to 1980. Over the course ofthe 1980s this region of the world has experienced dramatic declines in terms oftrade, devastating wars, and severe and prolonged droughts, in addition tostringent structural adjustment programs and escalating levels of debtrepayment.

The omission of war is particularly serious, whether one defines 'regress" ingrowth or welfare terms. A glance at the authors' list of "losers" (which, inaddition to such war-torn African countries as Angola, Ethiopia, Mozambique,Somalia, and Sudan, includes Afghanistan, El Salvador, Iraq, and Nicaragua)confirms that a high proportion has been engaged in some combination of civiland external wars. In southern Africa and the Horn of Africa war is the largestsingle economic fact (Green 1991). Although war is a topic from which econo-mists have typically shied away, we neglect it at our peril in any serious effort toaddress the economics of regress.

A dear illustration of the costs of war comes from the southern African region(comprising Angola, Botswana, Lesotho, Malawi, Swaziland, Tanzania, Zam-bia, and Zimbabwe). The economic costs of war in this region between 1980and 1988 have been estimated at approxinately $60 billion (in 1988 prices), anamount equivalent to twice annual gross domestic product (GDP) for all coun-tries in the region in the late 1980s and to three times gross external resourceinflows (grants, soft loans, export credits, and commercial loans) over 1980-S8(Green 1991). Between 70 and 75 percent of GDP losses were borne by two ofBarro and Lee's key losers-Angola and Mozambique. By 1988 more than 40percent of central government expenditures were allocated to defense inMozambique, a country with an annual per capita GDP of about $70 (USACDA

1992). War-related regress in Sen's terms was also staggering. According to theUnited Nation's Children's Fund (UNICE), there were 800,000 war-relateddeaths of infants and young children in Angola and Mozambique between 1980and 1988-the equivalent of crashing a jumbo jet fully loaded with childrenunder five every day over the entire period.

The wars in southern Africa are first and foremost a consequence of SouthAfrican aggression, induding the active support and massive funding of counter-insurgency movements. At the very least, the authors might wish to reconsidertheir unequivocal statements about the benefits of proximity to South Africa-particularly their observation that, if not for proximity to South Africa, Mozam-bique might have grown 8 percentage points below the mean rate instead of onlyS percentage points below. To omit a variable for reasons of computationalconvenience is one thing; to dismiss war atrocities with an econometnc sleight ofhand is quite another.

The case of southern Africa illustrates the importance of the regional contextin understanding the direct effects of war as well as the indirect effects-including disruptions of trade, the influx of refugees,and so forth-that do not

300 Comment

show up in country-level analysis. Apart from these and other costs, the spill-over benefits of proximity to South Africa may have less to do with access tocapital and skilled managers, as Barro and Lee suggest, than with migrant labormarkets. From the viewpoint of policy, the regional context is also essential.Reconstruction in southern Africa in the postapartheid era will require policies'that recognize and build on the mutually beneficial dimensions of regional inter-dependence. The same considerations apply in other parts of Sub-SaharanAfrica.

Political InstabilityWhat about the role of internal factors? According to Barro and Lee, the reasonthe model performs poorly in explaining regress in Sub-Saharan Africa is thatthe variables included to measure political instability and government distor-tions understate these difficulties in Africa. The effort to take account of politi-cal instability using the "revolution' variable is a particularly unusual feature ofthe model. This variable, we are told, measures the average number of success-ful and unsuccessful revolutions a year; yet precisely what is being measured,what it means, how it operates, and what its relationship is to economic growthare shrouded in mystery.

First, what constitutes a revolution? One's suspicion that it means very differ-ent things in different cases is confirmed by the data in Barro and Lee's table 6indicating that Nicaragua is 0.01 deviations from the mean, while the compara-ble figure for Korea is 0.17.

Second, Barro and Lee concede that economic adversity might have somethingto do with revolutions, but they dismiss the problem of biased estimates bysimply asserting that "the linkage is likely to be more from the level of income tothe propensity to revolt than from the growth rate to this propensity.' Why thisshould be so is left unexplained. Furthermore, the possibility that revolutionsmay have something to do with the distribution of income (and assets) is nevereven raised.

Third, we are told, in effect, that revolution is destiny. It appears that therevolution variable averaged over the full sample has more explanatory powerthan the average for each decade entered separately into each decadal equation.The logic invoked to explain this result is that the probability of revolutionremains roughly constant over time for a given country. Since precisely the samedata are being used in 1965-75 as in 1975-85 to pick up whatever it is that isbeing captured by the "revolution" variable, it is perhaps not surprising that theexplanatory power of the model declines over time for countries that haveexperienced escalating political unrest and civil wars.

In short, the variable for "revolutions" and its statistical significance remainboth tantalizing and mysterious. What all this does suggest, though, is theimportance of politi5:l factors for understanding economic processes and theneed for a much more nuanced understanding of how the institutions that

Hart 301

exercise power enter into economic processes. To this end, theoreticallyinformed comparative case studies (for instance, Berry 1993) will be a great dealmore informative than any effort to beat the "revolution" variable into shape.

Market Distortions and Government SpendingThe need for a clearer understanding of the institutions governing resourceaccess and allocation applies with equal force to the question of market distor-tions and government spending. Barro and Lee warn against the simplistic use ofregression results to deduce policy recommendations but use their results toendorse an unequivocally neoclassical policy position: remove market distor-tions and reduce government spending on consumption (except for educationand defense spending-what should happen to these is left open).

These conclusions could very easily be interpreted as meaning that what Sub-Saharan African economies in dire distress need is a good shot of structuraladjustment medicine. These are, of course, precisely the ideas that have shapedWorld Bank and International Monetary Fund policy toward Africa since theBerg report in 1981 (World Bank 1981). Although the active debate over theeffect of structural adjustment, particularly in extremely poor economies, isbeyond the scope of these comments, few would dispute that the effects ofstructural adjustment in much of Sub-Saharan Africa have fallen far short of itsproponents' claims. This is, indeed, precisely the conclusion of a recent WorldBank document (Elbadawi, Ghura, and Uwujaren 1992). The massive outflowof resources in the form of debt repayment is also beyond dispute.

Especially for African countries emerging from what has been termed the "lostdecade," further cracking of the neoliberal whip is unlikely to be particularlyhelpful. There are instead, two sets of policy considerations that would lead in amore constructive direction. First, if international agencies and the internationalcommunity are sufficiently serious about the phenomenon of regress to movebeyond the conference stage, one step would be debt relief along the linesrecently proposed by Helleiner (1992).

Second, there is a pressing need to move the policy debate away from thefundamentaily sterile terrain of big governments versus a pro-private sectorideology and to recognize that the dichotomy between the public and privatesectors is often a false one. The key questions that need to be understood moredearly are how different interests use the state to acquire and allocateresources-and, conversely, what are the social and political mechanisms ofconflict resolution and accountability that could lead to a more equitable andproductive use of resources.

ReferencesBerry, Sara. 1993. No Condition is Permanent: The Social Dynamics of Agrarian Change in Sub-Saharan

Africa. Madison, Wis.: Univasity of Wisconsin Press.

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Elbadawi, Ibrnhim A., Dhaneshwar Ghurn, and Gilbert Uwuinren. 1992. 'World Bank AdjustmentLcnding and Economic Performanec in Sub-Saharan Africa in the 1980s: A Comparison with OtherLow-income Countries." Policy Rescarch Working Paper 1000. World Bank, Policy ResearchDepartment, Washington, D.C.

Green, Reginald. 1991. "Neo-Liberalism and the Political Economy of War: Sub-Saharan Africa as aCase-Study of a Vacuum." In Christopher Colclough and James Manor, eds., Slates or Markets? Nea-Liberalism and the Development Policy Debate. Oxford, U.K.: Clarendon Press.

Hlelciner, Gerald. 1992. "The IMI:, the World Bank and Africa's Adjustment and External DebtProblems: An Unofficial View." World Deuelopmeni 26 (6): 779-92.

IUSACDA (U.S. Arms Control and Disarmament Agency). 1992. World Military Expenditures and ArmsTransfets. Washington, D.C.

Wheeler, David. 1984. "Sources of Stagnation in Sub-Saharan Africa." World Development 12 (1): 1-20.World Bank. 1981. Accelerated Deuelopment in Sub-Saha ran Africa: An Agenda for Action.

Washington, D.C.

Hart 303

COMMENT ON "LOSERS AND WINNERS IN ECONOMIC GROwTH,"BY BARRO AND LEE

Ishrat Husain

Iam delighted to be a discussant of the paper by Robert Barro and Jong-WhaLee. A whole endogenous growth industry has taken shape in the pastseveral years, and Barro's contribution to this industry is both impressive

and valuable. I would regard this latest paper as a continuation of the quest forenhancing our understanding as to why some countries grow faster than others.As a policy economist working on Africa, an area that has both grown lessrapidly than other developing regions and has a disproportionate share of slow-growing countries, I am particularly attracted to the line of inquiry adopted inthis paper.

I find myself in agreement with the general condusions presented in the paper:that policy distortions, political instability, and the size of government affectgrowth negatively, and that human capital and physical investment have posi-five effects. I am less persuaded by the net convergence hypothesis, as the Afri-can countries have demonstrated a growing divergence and other studies tend tofind evidence to the contrary.

I would like to offer a few observations about the model specification and themeasurement of variables and omitted variables, without questioning the meritof cross-country regressions for specific country or regional policy inferences.

First, I wonder whether time-series variation within each country that cap-tures country-specific effects in the cross-sectional studies might enrich theanalysis and yield different conclusions about the magnitude, the significance, orthe sign of the coefficients.

Second, I would also suspect that, given Africa's dependence on-and concen-tration of exports in-primary commodities, countries in that region are moreprone to terms of trade effects. In my view, these external shocks explain asmuch of the variance in growth rates as do economic policies. The question is,would the inclusion of the terms of trade variable make any difference? Some

Ishrat Husain is chief economist in the World Bank's Afirica Region.

Proceedings of the World Bank Annual Conference on Development Economics 1 993@ 1994 The Intemaional Bank for Rccnstuction and Development / THE VRLD BANK 305

studies have shown that including shock variables in the regression reduces thecoefficient on the black-market premium and significance levels, especially forthe 1980s.

Third, Levine and Renelt (1992) find that cross-country studies are highlysensitive to slight alterations in the right-side variables. They show that thenegative association between governmcnt consumption and long-run growthbreaks down when monetary policy indicators are induded. In a similar vein,Easterly (1993) and de Gregorio and Guidotti (1992) have also reported thatfinancial development has a positive effect on economic growth. Thus my ques-tion is, what happens to Barro and Lee's results if external shocks, financialdevelopment, and fiscal policy variables are introduced in the model, providedthere is no multicollinearity between the financial devclopment and fiscal policyvariables? My guess is that if we were running the regressions for just the Africaregion, the fiscal deficit would have a strong effect. I will lacer argue that thegovernment consumption variable as used in this model is not an appropriateproxy for fiscal policy.

I now turn my attention to the four right-side variables that are proxies forpolitical instability, policy distortions, government size, and investment.

Political Instability

Barro and Lee measure political instability by the number of revolutions orattempted revolutions. I doubt, however, that this provides the right measure.Take Ghana and Zaire, for example. According to my calculations Ghana expe-rienced numerous coups and attempted coups during the first five years of theRawlings regime, but in terms of policy continuity and implementation it hasbeen the most stable regime politically. The projected growth rate for Ghana for1985-9S in the regression is -3 percent, but in fact, between 1985 and 1992Ghana grew by S percent a year. By contrast, Zaire, with no serious coups orrevolutions since President Mobutu took power, has seen policy discontinuities,erratic behavior, and inconsistencies so severe that they virtually wiped out theconditions under which modest growth could occur. I suggest that perhaps thetype of study done by Alesina and Perotti (1992), with an index of socio-economic stability, is a better measure of political stability than the one used inthis paper. Many countries in Africa have been hit hard by internal strife, civilwars, and ethnic tensions that cannot be adequately captured by WAR dummiesand that may have been a major reason for the economic dislocation which hascontributed to negative growth. The work we have done in the region to disag-gregate and study this group of countries clearly brings out this point.

There are those who subscribe to the hypothesis of joint endogeneity; that is,political instability affects aggregate economic outcome, but the latter also influ-ences executive instability. Low growth itself may be a determinant of socialunrest that disrupts market activities, increases economic uncertainty, and pro-

306 Comment

motes executive instability. Long and violent stiikes, riots, and physical threatsto workers affect productivity and, therefore, the rate of return to investment.

Policy DistortionsI have some problem with Barro and Lee's use of the black-market exchange ratepremium as the sole indicator of policy distortions. The black-market premiumis valid only on one side of the market, and this produces asymmetrical effects.Under a controlled exchange rate regime the administrative allocation of foreignexchange at the official rate for imports is one of the main sources of thispremium. The supply to the black market therefore takes place through alloca-tions for imports. The effective exchange rate paid by actual importers (notlicensees) is thus correlated with the black-market rate. But the effectiveexchange rate received by exporters, who are required to surrender their foreignexchange to the central bank, is still the much lower official rate. This wedgebetween the two effective exchange rates is quite large in many countries. Andbecause the exporters' earnings are not part of the black-market supply, thetransaction costs and risk premiums are high, the market is often thin, andtherefore the black-market premium is not always a valid measure of the magni-tude of exchange rate distortion.

The other problem is that even when the black market premium is zero, as isthe case within the CFA zone, overvaluation may be substantial and policy distor-tions quite sharp. The black-market premium will thus fail to capture thisapparent anomaly.

I believe that there are other deep-rooted policy distortions-beyondexchange rates-that make a huge difference to growth rates. For example, ifexchange rates are market determined but producer prices of agriculturalexports remain fixed at depressed levels by a parastatal monopoly, the incentivestructure will continue to be unfavorable, and the supply response will beunchanged. Even though exchange rates are no longer distorted, exports areconstrained, and if there is a positive relationship between exports and growth,growth rates will remain static.

Government SizeBarro and Lee exclude military expenditure from the government consumptionvariable on the grounds that it provides security to the state. In fact, the level ofmilitary expenditure is one of the most unproductive components of publicexpenditure in Africa. Not only is the level high in many cases-in Angolamilitary spending accounts for 25 percent of gross domestic product (GDP)-but

it also competes direcdy with and in some instances crowds out expenditures oneducation, health, nutrition, and so on. Since the productivity of this expendi-ture is questionable, exduding it from the regressions may, in fact, understatethe negative influence of government consumption.

Husain 307

Invcstmcnat

The last variable I would like to discuss is the ratio of investment to GDP, I wouldsubmit that the investment ratios of the 1970a were clearly unsustainablebecause tely were financed by petrodollar recycling-not a stable source ofexternal financing. But more importAnt, this large flow of inventment beyondthe countries' absorption capacity led to questionable choices of projects andeventually contributed to the debt crisis of the 1980s. Decisions on investmentlevels should be made on tin informcd basis; the type of investment-equipmentcompared to otier investments, for example-is equally significant (see De Longatnd Summers).

The level of investment is not likely to be the main determinant of growth inAfrica: thc efficiency and productivity of investment are more critical. In asample of adjusting countries that the World Bank reccntly analyzed, investmentratios during the early 1970s averaged 20 percent, but the growth ratc wasabout 3.5 percent. In the late 1980s investment ratios had declined to 1S per-cent, but the growth rate had risen to 4 percent. Why has this happened? Thedata for estimating total factor productivity (TPP) in Africa are not rcadily avail-able, but my guess is that rnmP growth was negative in most of the countries in the1970s but that there was some modest improvement in the late 1980s. Althoughinvestment share and investment growth have declined, reallocation from indus-try to agriculture in the 1980s has contributed to an improvement in Tfl'.

Despite this modest improvement, private investment levels are low comparedwith the requirements. Macroeconomic instability and uncertainty still poseserious problems, reducing the efficiency of the price mechanism and the rate ofincrease in productivity, as well as the enthusiasm of private investors. On thefinancial side, the dominant role of the government, through its burgeoningbudget deficits and public enterprise losses, crowds out private investment. Thecapital requirements of private sector industries and agriculture remain unmet,further depressing the level of investment.

Why a Negative Dummy for Africa?A final question concerns the negative dummy for Africa. Easterly (1993) hasshown that the Latin America dummy is not significant when the black market,the ratio of government surplus tO GDP, and the ratio of M2 to inp are indudedin the regression. But the African dummy still persists. I would speculate thatthere is a minimum threshold effect below which the relationship between long-term growth and the kind of right-side variable we have been discussing is notvalid. The convergence result may apply to countries above that threshold, butcountries below a certain income level (and lacking certain human capitalendowments) do not enjoy the extra growth associated with low income. Inother words, there may be some nonlinearities or step-functio1s present thathave not yet been adequately captured or some important interactive terms that

308 Comment

need to be taken into account. The point is that in Africa not only are outwardorientation, human capital, macroeconomic stability, and domestic competitionweak, but the interactions between them do not produce the extra growthassociated with the convergence effect Alternatively, policies are so poor thatthey limit growth rather than facilitate convergence.

Jong-Wha Lee, the coauthor of the paper, has studied the relationshipbetween growth and trade distortions induced by such government policies astariffs and cxchange controls (Lee 1992). He has concluded that these distor-tions generate significant cross-country divergences in growth rates and in percapita income over a long transitional period. I would therefore tend to thinkthat Fischer's (1993) production-function-based approach, which helps to iden-tify the channels through which macroeconomic variables affect economicgrowth, will be more helpful in explaining the laggard performance of Africaneconomies. In Fischer's framework, growth can be attributed to increases insupplies of factors and to a residual productivity category that reflects changes inthe efficiency with which factors are used. Adverse changes in the terns of tradereduce growth mainly through their effects on productivity growth.

I think that the magnitude of economic policy distortions, macroeconomicinstability, and the dominant size of the government-correctly measured-would explain most of the variance in the growth rate of African countries.

ReferencesAlesina, Alberto, and Roberto Pcrotti. 1992. "Income Distribution, Political Instability and Investment."

irR Working Paper Series. Institute for Policy Reform, Washington, D.C.

Dc Gregorio, Jose D., and Pablo E. Guidotti. 1992. 'Financial Development and Economic Growth. IMF

Working Paper. International Monetary Fund, Washington, D.C.De Long, J. Bradford, and Lawrence H. Summers. 1991. 'Equipmcnt Investment and Economic

Growth." QuarterlyJournal of Economics 106 (May): 445-502.

Easterly, William. 1993. "Is Africa Different? Evidence from Growth Regressions." World Bank, PolicyResearch Department, Washington, D.C.

Fischer, Stanley. 1993. 'lMacroeconomic Factors in Growth." Paper presented at the World Bankconference, "How Do National Policies Affect Long-Run Growth?" Washington, D.C., February.

Lee, Jong-Wha. 1992. "International Trade, Distortions and Long-Run Economic Growth." imrFWorking Paper. International Monetary Fund, Washington, D.C.

Levine, Ross, and David Renelt. 1992. "A Sensitivity Analysis of Cross-Country Growth Regressions."Amenican Economic Review 82 (September): 942-63.

309

FLOOR DISCUSSION OF THE BARRO-LEE PAPER

A participant, noting that his own comparison of growth rates of grossdomestic product (GDP) for all oil-exporting countries before and afterthe oil shocks showed a decline of 5 percent a year after oil prices started

to fall, expressed surprise at Barro and Lee's finding that changes in terms oftrade had no effect on GDP. The participant had found that an improvement interms of trade tends to lead to an increase in the shares of investment andgovernmcnt spending in total output. That Barro and Lee had included thosetwo variables in their regressions, he said, might account for their not mention-ing the effect of changes in terms of trade.

The measures being explained in the paper, responded Barro, are related toproduction, tO GDP. There is no mechanical link between GDP and the growthrate of GDP or between GDP and a change in the terns of trade. If there were achange in the export price of a primary product, for example, and the countrydid nothing to change the output mix, the changing terms of trade wouldbasically have no effect. Of course, real income and consumption would belower, but the link is not mechanical. For terms of trade to have an effect, peoplehave to respond to incentives by, for example, producing less of something whenthe price of that export goes down or, when the price of an input such as oil goesup, importing less of that input and settling for less output. Barro was willing tobelieve that changes in terms of trade might be important, and he was trying toimprove on the data he normally used, which were inadequate. He agreed thatother variables in the data set, including the black-market premium, probablypick up the effect of changes in the terms of trade, to some extent, which wouldhelp explain why the effect does not appear to be great. As for the effect of theoil shocks, the change in terms of trade might have a separate effect only to theextent that it would have differential effects crosssectionally or in different timeperiods (for example, 1965-75 versus 1975-85).

The session was chaired by Armemne Choksi, vice president, Human Rcsources Development and Opera-tions Policy, the World Bank.

Proceedings of the World Bank Annual Conference on Development Economics 1993© 1994 The Intenational Bank for Reconsruction and Development / THE WORLD BANK 311

Barro said he was pleased that part of the discussion was about the measure-ment of several kinds of variables. Frequendy, in measuring a certain effect, thechoice is between using a variable such as the black-market premium on foreignexchange that is objectively measurable and fairly quantifiable but is not quitewhat is wanted and using other more qualitative variables that are often con-structed after the fact. An example of the latter would be the World Bank'sdassifications of how open countries are in various respects. Barro noted thatvariables of the second kind can fit too well because one has already peeked atthe data before measuring. Barro was sympathetic to the idea that some of thevariables he used were not exactly what one would want. That is why hestressed that some of them were proxies for other, more general, kinds ofinfluence.

Overall relationships are, of course, what emerge from regression estimates;that is why regressions are done. Barro thought that picking out residuals andtelling stories about them and calling them case studies was not especially infor-mative. Noting the existence of some residuals was not really interesting. Sug-gestions of variables with broader effects that might be included in the overallsample would be more helpful.

Barro was sympathetic to the idea of looking at terms of trade and at thewARTIME variable, both of which he thought might be important. The onlyquantitative measure he was able to get that worked for the large sample ofcountries was a measure of whether a country had been involved in an externalwar. Two war variables were included in some of the regressions in table 5, butthey had no explanatory power; a more precise measure of the intensity of warmight work better.

The revolution variable came from the Arthur Banks (1979) data set, a 1979reference for which data were updated to continue past 1985. The variable wasconstructed by compiling newspaper reports and deciding whether a reportedevent constituted a revolution or something else such as a riot, a political assas-sination, guerrilla warfare, or constitutional change. Those were the kinds ofvariables that Alesina and Perotti (1992) had used to compile an index of politi-cal instability. Barro had no quarrel with Alesina's variable but said it was notfundamentally different from the one he was using.

Ishrat Husain had questioned the political instability index, noting thatGhana, despite having had more revolutions and failed coups than Zaire, hadgreater policy continuity and credibility in terms of implementation than Zaire,which had not experienced a change in government between 1965 and 1992.Actually, said Barro, the revolution variable for Ghana and Zaire was almostthe same because the variable includes both unsuccessful and successful efforts.But it was true that the variable could be improved.

A participant from Nigeria noted that the data set used by Barro and in otherpapers on growth measures the efficiency of GDP to a large extent. An explana-tion for the large unexplained residual observed for the African regressions for1965-85, he said, might be that during the period an increasing share of GDF

312 FloorDiscussion

was moving out of the official market. Thus the unexplained residual maysimply reflect the kind of data being used. Barro said he was using standardsources, which have well-known shortcomings, and that there might well be ameasurement error. He would have expected that a move into unreported black-market activities would have been somewhat counterbalanced by a move in theother direction, reflecting the tendency for households to move away from workat home and from agricultural production for the household's own use-a shiftwhich in poor countries would have raised growth rates.

As for the government consumption variable, Barro said, he was trying to geta measure that was not directly productive in the sense of influencing what wentinto the production function. That was why he removed educational spending,in particular, because any reasonable person would acknowledge that educa-tional expenditure did in- fact have a productive purpose and was not only aconsumptive good.

Government spending on defense was more complicated, said Barro. Somepeople think defense, by improving national security and hence property rights,encourages investmnent; others think defense is a response to a military threatfrom other countries, a situation that discourages investment. He did notinclude government spending on defense as a proportion of GDP as a separatevariable because its coefficient in a growth regression is about zero.

Following up on discussant Gillian Hart's comments, a participant from Tan-zania asked what, if any, was the effect on growth of membership in the SouthAfrican Customs Union and the rand monetary area? Botswana, Lesotho,Swaziland, and parts of Malawi and Mozambique participate in the SouthAfrican labor market. What effect does the rapidly increasing number of layoffsin South Africa have on growth? And what is the effect of foreign investments ongrowth in South Africa's neighbors? South Africa has invested heavily in infra-structure in Botswana, Lesotho, and Swaziland, which it considers to be exten-sions of the South African economy; it has not invested in Angola, Mozam-bique, or Tanzania. Barro responded that the residual for Tanzania was aboutzero in the regressions, suggesting that it did not pick up the same effects asBotswana, Lesotho, Swaziland, and perhaps Mozambique. Barro had thoughtabout spillover effects, in terms of investments and the labor market. Theseeffects were especially important when there was a big gap between the incomesof neighboring countries.

Another participant asked what sort of premium Barro found on backward-ness and how sensitive the model might be to substitution of an initial growthrate for capital stock. Barro said he had not used capital stock because he did notthink the numbers on capital stock were any good. The measurement problemfor capital stock was more serious than for other variables, which was why hechose the formulation he used.

The same participant asked a question about Barro's subgroups. Barro hadsaid that the data confirmed that the specifications for slow-growing countrieswere pretty much the same as for the subgroups. But the t-values might not be

Floor Discussion 313

the same, said this participant. Recalling Barro's 1991 article, this participantfelt that as one disaggregated and used smaller countries with lower incomesthere might not be a change in the signs of the variables Barro included in hisspecifications but the t-values would tend to deteriorate. Would Barro considerdividing these countries into "actually depressed," "financially depressed," and"mildly depressed," as was suggested in the Stiglitz discussion? Barro respondedthat there were some differences if the countries were grouped into high- andlow-income categories, if one looked across the coefficients. But more strikingwas the similarity; in particular, the estimated conversion speeds were almostthe same, and the overall test confirmed that the two could be combined, whichwould yield much more precise estimates. That is why much higher t-ratios areobtained, typically, when the whole sample is used, assuming that behaviors arenot so different. Barro said that he found this much more informative.

References

Alesina, Alberto, and Robert Peroti. 1992. 'Income Distribution, Political Instabaity, and Investment."Institute for Policy Reform Working Paper. Washington, D.C.

Banks, A. S. 1979. 'Cross-Nadonal Time-Scrics Data Archive." Center for Social Analysis, StateUniversity of New York, Binghamton.

Barro, Robert J. 1991. "Economic Growth in a Cross Section of Countries." Quarterly Journal ofEconomics 106 (May): 407-43.

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