Economic growth in Latin America

Journal of Development Economics 39 (1992) 59-84. North-Holland

Economic growth in Latin America*

Jo& De Gregorio

International Monetary Fund, Washington DC, USA

Received March 1991, linal version received December 1991

This paper studies growth determinants in 12 Latin American countries during the period 195&1985. In a growth accounting framework, the share of labor in income is found to be lower in the sample group than in developed countries, while factor productivity growth accounts for a larger proportion of growth in the fastest growing countries. Using panel data, macroeconomic stability is found to play, in addition to investment (physical and human), a crucial role in growth. To a lesser extent, growth is negatively correlated with government consumption and political instability. The terms of trade appear to have no significant effect on growth.

1. Introduction

In the last few years the literature on economic growth has been characterized by numerous and interesting new developments, as well as by the rediscovery of old, though somewhat forgotten, insights.’ In the empirical field, many papers have attempted to test the implications of economic growth theories and to determine the main sources of growth using cross-section data for large samples of countries.

This paper studies growth determinants in Latin America.2 Mainly because of data limitations, this paper focuses on 12 countries during the 195C-1985 period (see table 1). Latin America provides a relatively homo- geneous sample of countries with enough policy experiments to adequately assess the sources of growth that have been analyzed in larger cross-sections of countries. In studying the growth experience of Latin American countries, several approaches are used. First, some basic indicators bearing on growth performance are examined. Next, growth accounting exercises are under-

Correspondence to: Dr. Jo& De Gregorio, International Monetary Fund, Washington, DC 20431, U.S.A.

*This paper was prepared for the fourth meeting of the Interamerican Seminar on Economics, Santiago, Chile, March 15-16, 1991. I am very grateful to Rudi Dornbusch and Peter Wickham for valuable comments and extensive discussions. I also would like to thank Eliana Cardoso, Juan Eduardo Coeymans, Jest Fajgenbaum, Albert Fishlow, Jordi Gali, Pablo Guidotti, Ross Levine, Manuel MarfAn, Carmen Reinhart, Miguel Savastano, And& Solimano, John William- son, and participants at the Seminar for helpful comments, and Catherine Fleck for editorial assistance. The views expressed in this paper do not necessarily represent those of the IMF.

‘See, for example, Romer (1986), Lucas (1988) and Rebel0 (1991). ‘1 have found few studies about sources of growth in Latin America. See Cardoso and

Fishlow (1989) and the references therein.

03043878/92/$05.00 0 1992-Elsevier Science Publishers B.V. All rights reserved

J.D.E.- c

Tab

le

1

Bas

ic

indi

cato

rs

for

Lat

in

Am

eric

a,

195&

1985

.”

____

~ G

DP

per

capi

tad

GD

P”

GD

P pe

r ca

pita

b Po

pula

tionb

T

erm

s of

tr

ade”

In

vest

men

t’

~~

~___

_~

Arg

entin

a 2.

4 0.

8 1.

6 -1

.2

25.1

Bol

ivia

2.

1 0.

2 2.

4 2.

3 12

.9

Bra

zil

1.5

4.1

2.1

-2.3

24

.4

Chi

le

2.8

0.9

1.9

- 1.

4 29

.1

Col

ombi

a 4.

9 2.

3 2.

6 -0

.5

18.8

Cos

ta

Ric

a 5.

6 2.

4 3.

1 -0

.5

14.2

Ecu

ador

5.

7 2.

8 2.

8 -1

.8

24.1

Gua

tem

ala

4.0

0.9

3.0

-2.1

8.

8 M

exic

o 5.

8 2.

5 3.

1 0.

9 18

.8

Peru

4.

0 1.

5 2.

5 1.

3 13

.2

Uru

guay

1.

5 0.

5 0.

9 -1

.3

12.1

V

enez

uela

3.

5 -0

.2

3.7

3.4

11.7

~_

__

“All

data

fo

r G

DP

and

GD

P pe

r ca

pita

ar

e in

re

al

term

s.

bGro

wth

ra

tes

in

perc

ent

per

annu

m.

‘As

a pe

rcen

t of

G

DP.

dP

erce

nt

per

annu

m.

‘Per

iod

aver

ages

in

19

80

U.S

. do

llars

. So

urce

s:

Sum

mer

s an

d H

esto

n (1

988)

, E

CL

AC

, an

d IF

S.

Infl

atio

n”

1950

-195

2

15.3

62

.5

1,05

1 55

.5

53.0

14

.8

1,19

4 9.

0 1,

208

9.2

938

4.4

1,15

4 15

.2

1,11

6 25

.4

1,29

6 40

.4

3,08

4 4.

8 4,

024

1959

-196

1 ~~

3,

069

881

1,11

5 2,

893

1,34

8 1,

652

1,12

5 1,

212

2,13

1 1,

693

3,24

1 5,

374

197&

1980

4,19

8 1,

441

2,59

8 3,

648

2,13

8 2,

112

2,07

0 1,

754

3,59

2 2,

408

3,72

9 5,

339

J. De Gregorio, Economic growth in Latin America 61

taken, and finally, estimations using panel data are carried out. The main findings, not all of them original, are as follows:

- The rate of income growth in Latin America has been comparatively modest during 195&1985. _ Growth has been higher in countries where the shares of industry and exports have had the largest increase, and where the change in the share of agriculture has been the lowest. _ There is no evidence of (unconditional) convergence of per capita income across Latin American countries. _ Labor’s share is about 50 percent of income, which is substantially lower than in developed countries. _ The proportion of growth explained by factor productivity growth increases with the rate of growth itself. This finding is inconsistent with traditional versions of the neoclassical growth model. ~ Investment is one of the main determinants of growth, but its components have a differential impact. Foreign investment appears to be more efficient than domestic investment. _ The terms of trade appear to have no significant effect on growth. _ The level of inflation, as well as its variability, have negative effects on growth, beyond their possible negative effect on the rate of investment. This result is valid in general and not only in countries which have experienced high rates of inflation. _ Human capital, measured by literacy rates, has a positive effect on growth. Paradoxically, school enrollment indices have no positive relationship with growth. _ The degree of openness of the economy and the distribution of income are found not to have significant effects on growth. ~ The effect of government consumption on growth is negative. The degree of political stability, measured as an increase in civil and political rights, is positively correlated with growth. These results are, however, less robust than the others reported here.

The rest of this paper is devoted to explaining the above findings and substantiating them empirically. 3 The results are qualified and interpreted

in the light of recent developments in the theory of economic growth. The paper is organized in six sections. Section 2 presents some basic facts and data about countries in the sample. Section 3 examines growth accounting in the Solow tradition. Sections 4 and 5 take a broad look at growth determinants using panel data. The role of macroeconomic instability (measured mainly by inflation), government spending, investment, political

3Further evidence to support the main findings is presented in a more extended version of this paper, De Gregorio (1991a).

62 J. De Gregorio, Economic growth in Latin America

Table 2

Structure of production and demand in Latin America, 1965 and 1985 (in percent of GDP).

Agriculture

1965 1985

Industry Manufacturing Services Exports __~_ -~ _.~

1965 1985 1965 1985 1965 1985 1965 1985

Argentina Bolivia Brazil Chile Colombia Costa Rica Ecuador Guatemala Mexico Peru Uruguay Venezuela

12 11 98 33 29 23 50 56 8 15 19 24 40 29 13 13 41 46 21 18 22 12 36 39 28 29 41 49 8 14

9 8 42 39 25 20 50 53 14 29 23 19 32 33 22 22 45 49 11 15 23 19 21 26 55 55 23 32 22 12 23 42 12 17 55 46 16 27 28 26 19 20 16 16 53 54 17 19 12 8 29 32 20 21 59 60 9 16 15 12 39 39 21 19 46 49 16 22 13 13 30 27 23 57 60 19 25 4 6 60 43 11 19 37 51 31 27

Sources: The data on exports are taken from the World Development Report 1987, World Bank. Remaining data from the 198771988 and 1989-1990 editions of the World Bank’s World Tables.

variables and other factors are considered. Finally, section 6 provides some concluding remarks.

2. first look the evidence


Table 3

Growth rates in selected countries, 195&1985 (in percent per year).”

GDP Per capita GDP

Belgium 3.4 3.0 Canada 4.2 2.4 France 4.2 3.4 Germany 4.6 4.0 Greece 5.2 4.4 Hong Kong 9.3 6.8 Japan 1.4 6.3 Korea 1.4 5.1 Singapore 9.6 7.7 Taiwanb 9.6 6.6 Turkey 5.9 3.3 United Kingdom 2.6 2.2 United States 3.3 1.9

“Data for Hong Kong and Singapore are for 196&1985, while for Korea the period covered is 1953-1985.

‘Taiwan Province of China. Source: Summers and Heston (1988).

Another characteristic of most Latin American countries is macroeconomic instability. Only four countries have had average inflation in single digits, while in others it has averaged above 50 percent. In the latter group several countries have also had periods of triple digit inflation and hyperinflation. As is shown later, inflation and its variability have significant effects on long- term growth.5

The sectoral structure of production and the share of exports in output is shown in table 2. There is a clear change in composition away from agriculture towards services. Between 1965 and 1985, the share of agriculture decreased in 9 countries, while in 11 countries the share of services increased.

Comparing growth performance and output composition during the period 196551985, it can be seen that the output share of industry increases in all fast-growing countries, while it decreases in those growing more slowly. This relationship does not hold, however, with respect to manufacturing. For agriculture, the correlation between the change of share and growth is negative. There is also a positive correlation between the change in export share and per capita growth. Thus, growth seems to be correlated with industrialization, an increase in the share of exports and a diminishing role for agriculture, regardless the initial structure of production. Note, however, that there is no causal relationship in this empirical regularity. In fact, the evidence to be presented later does not support a link from the structure of

5A negative effect of inflation on growth has also been reported in Kormendi and Meguire (1985), Fischer (1991), and Roubini and Sala-i-Martin (1991), among others.


production to growth; this finding suggests that the relationship may be from growth to sectoral composition.

3. Neoclassical growth accounting

A logical starting point for investigating the basic facts on growth is an examination of its sources. Although there is renewed interest in explaining growth across countries, there are no recent growth accounting studies for Latin America. A traditional Solow decomposition [Solow (19.57)] is the starting point for evaluating the relative contribution to the rate of GDP growth of factor input growth and total factor productivity growth.

Consider the following standard growth decomposition:6

y=al+(l-a)k+O, (1)

where y, k and 1 are the rate of growth of output, capital and labor inputs, respectively. 0 is a constant rate of productivity growth and CI is the share of labor.

The standard approach to growth accounting is to obtain input shares directly from the data. Then, by applying historical growth rates of capital, labor and other inputs, total factor productivity growth is obtained as a residual. Because of the lack of reliable data on factor shares and capital stock, k in (1) is replaced by iY/K, where i is the investment rate and Y/K is the outputcapital ratio. Then, eq. (l), written in regression form, becomes

where PI is equal to a and p2 is equal to (1 -a) Y/K. Note that this transformation implicitly assumes a constant capital-output ratio. This equation makes it possible to recover factor shares and the capital-output ratio from a regression analysis, which can then be used to perform growth accounting.

The primary data source is Summers and Heston (1988), where yearly data, covering the period 195&1985 for most of the countries, are provided. The data have been pooled, assuming that the underlying technology is the

6This decomposition implicitly assumes constant returns to scale and marginal cost pricing. See Hall (1988).

.I. De Gregorio, Economic growth in Latin America 65

Table 4

Regression results.a.b

Regression Method’ b, KIYd Number of observations

1 SUR 0.490 0.403 1.27 312 (2.981) (8.403)

2 SUR 0.387 0.454* 1.35 312 (2.283) (-)

3 LSDV 0.575 0.379 1.12 354 (1.403) (5.204)

“Notes of the regressions: 1. Equal coefficients, except constant. 2. Different coefficients, except PI. *Represents the simple average (across countries) of the estimates of the coefficient bz. 3. Equal coefficients, except constant.

“t-statistics in parentheses. “SUR: seemingly unrelated regressions; LSDV: least squares dummy variables,

includes one dummy per country. dK/Y: capital-output ratio.

same across countries, except for the constant. The results are presented in table 4.

The first two regressions report the results of using the seemingly unrelated regression technique (SUR), taking into account that technical progress, in spite of being different across countries, is correlated. Regression 1 considers that the only technological difference across countries lies in total factor productivity growth. The share of labor appears to be close to 0.5. Regression 3 is similar in the sense that it pools the data and adds a dummy per country. As a result, it does not take into account correlation of the residuals in each period, but assumes that factor productivity growth is the only difference in the production function across countries. In this regression, the labor share is 0.58. Regression 2 assumes that in addition to productivity growth, the capital-output ratio differs across counrties; the lowest labor share is obtained in this case. The general fit is poor, which is not surprising since many sources of short-run output variability are excluded by using annual data. In the least squares regression 3 the R2 is 0.18.

The results suggest that labor share in Latin America lies between 0.39 and 0.58. These values contrast strongly with those that had been obtained for developed countries. Evidence for Japan, the U.K., and the United States show labor shares in the 70 or 75 percent range.’ The results reported here confirm evidence from input-output matrices for developing countries, where the labor share appears to be around 50 percent to 60 percent at most. I interpret this low labor share as being explained by the existence of a larger

‘See Maddison (1987) and Romer (1989). The latter also reports evidence that Japan, the U.K., and the United States had a labor share of about 60 percent one hundred years ago.

degree of imperfect competition and increasing returns to scale in developing countries compared to industrialized economies. In other words, growth in LDCs would be accompanied by an increase in the degree of competition and a larger exploitation of scale economies.

Hall (1988) has stressed the importance of the assumption of perfect competition and constant returns to scale in growth accounting and the difference that arises when using cost shares as opposed to revenue shares. To show how this effect may be important and provide some rough measurement, consider income to be composed of labor income (wL), capital income (rK) and pure profits (n). Note that the last of these is caused by the wedge between marginal cost and prices and the existence of scale economies. Total income is

Y=rK+wL+lc.

The common accounting practice is to first measure labor costs and then assign the residual to the contribution of capital. In this case, the following labor share in income would be obtained (CI, to distinguish from cost share, which is a):

c$= wL/(wL+rK+n), (4)

while

a = wL/(wL + rq. (5)

Thus, a higher proportion of profits in revenue will reduce the observed labor share. Since the dependent variable is income instead of costs, the estimates are capturing income shares. A simple calculation can illustrate the magnitude of the degree of competition and of the effects of increasing returns to scale. Assume that labor share in costs is, as in developed countries, between 0.65 and 0.70. As a benchmark case, developed countries are also assumed to be competitive and face constant returns to scale. Then, a labor share in income between 0.45 and 0.55 would imply that noncompetitive profits and increasing returns to scale account for between 15 percent and 35 percent of total income. Hence, another possible source of growth, not accounted for in the previous results, could be the gain in competitive- ness and fuller exploitation of scale economies as output grows.

There are also other factors whose contribution is not measured. The most important of these is human capital. In contrast to the pure profits case, however, there is no reason to believe that exclusion of human capital generates a bias toward increasing the share of capital.

Protectionist policies in less developed countries may help to explain why the degree of competition increases along with the level of development. The


increasing market size may also explain why monopoly power and the extent of scale economies may fall with growth.

There is an alternative explanation for the low labor share observed in developing economies. The explanation is based on the existence of independent workers, whose income would be imputed to capital rather than labor. Consequently, the labor share would be underestimated. Harberger and Wisecarver (1977) found that in Uruguay during 1967-1971 the return to capital would have been overestimated by 23 percent on account of independent workers. Therefore, if the true capital share were 30 to 35 percent, the observed return would have been 37 to 43 percent and the labor share would be around 0.57 and 0.63. Even under the extreme case that all independent worker income is correctly included as labor income in developed countries (i.e., the factor shares are correctly computed), the discre- pancy between developed and developing countries can not be fully accounted for. Hence, the independent-worker effect is not enough to explain the differences in labor shares across countries at different stages of development, although, the existence of this effect will reduce the above estimates of the share of rents from scale economies and noncompetitive profits.

The capital-output ratio implicit in eq. (2) is computed as (1 -/Ii)//& and is presented in the fifth column of table 4. The largest value for this ratio, 1.35 in regression 2, is still low by intentional standards. Estimates of this ratio usually fall between 2.5 and 4.

Table 5 reports the results of growth accounting using the parameter estimates from regression 1. During the period 1950-1985, Latin America grew by a (simple) average rate of 4.2 percent, of which 51 percent is explained by investment, 30 percent by population growth, and the remaining 19 percent by productivity growth.8

There are differences across the Latin American countries of the sample. The most important regularity is shown in fig. 1, where the average growth is found to have a strong positive correlation with the relative contribution of productivity growth (column 5, table 5). The live fast growing economies (with growth rates above 4.9 percent) all have a productivity contribution above 20 percent. In the remaining seven economies (with growth rates below 4 percent), productivity growth has a contribution of under 20 percent. This relationship is in contradiction with the simplest neoclassical growth model, according to which no correlation should be expected. Fig. 1 indicates that some form of endogenous growth exists: productivity growth is proportionally more important, the higher is the growth rate. Since growth increases

*The results are somewhat different than those of Chenery, Robinson and Syrquin (1986) for a sample of 20 developing countries, in particular in the relative contribution of productivity growth. See Denison (1985) for evidence on the U.S., and Dornbusch and Park (1987) for evidence on Korea.


Table 5

Growth decomposition, period: 195Ckl985 (in percent).a ~.

GDP growth Population Investment Productivity

(1) (2) (3) (4)

Argentina 2.4 0.8 1.2 0.4 Bolivia 2.1 1.2 1.4 0.1 Brazil 1.5 1.3 3.8 2.4 Chile 2.8 0.9 1.4 0.4 Colombia 4.9 1.3 2.5 1.1 Costa Rica 5.6 1.5 2.8 1.2 Ecuador 5.1 1.4 2.9 1.4 Guatemala 4.0 1.5 2.0 0.5 Mexico 5.8 1.5 2.9 1.3 Peru 4.0 1.2 2.1 0.8 Uruguay 1.5 0.4 0.7 0.3 Venezuela 3.5 1.8 1.8 -0.1

Averageb 4.2 1.2 2.1 0.8

Avg. 5tk70b 5.3 1.3 2.1 1.3 Avg. 7&85b 2.9 1.1 1.5 0.3

_ “Discrepancies in the sums are due to rounding. ‘Simple average.

(4)/( 1) (x100) (5)

16 4

32 15 23 21 25 12 22 19 18

-3

19

24 10

with investment, the relationship observed in fig. 1 can be explained by the omission of a variable positively correlated with investment and growth.’

Table 5 also shows growth decompositions for the sample of Latin American countries during the periods 1950-1970 and 197&1985. The first period is one of faster growth, which is mostly accounted for by higher rates of investment and productivity growth. As expected, the role of population growth is more stable. It should be noted, however, that growth decompositions do not change significantly when other sets of parameters are used. Particularly remarkable is the fact that the role of productivity growth decreases substantially during the period 197&1985. This confirms the result shown in fig. 1 in a time series context: periods of low growth are characterized by a relatively small contribution of productivity growth. Since this suggests that changes in the path of growth are due to a large extent to changes in total factor productivity growth, a further examination of the determinants of growth is warranted.

4. Growth determinants: Preliminary considerations

The empirical question posed by the new literature on endogenous growth is whether productivity growth is in fact exogenous or whether its economic determinants can be identified. The systematic relationship between the share

‘This would also account for a downward bias in the estimate of the capital-output ratio.


GDP Growth

8

7 -

6 - l l l

5 - 0

-5 0 5 10 15 20 2s 30 3s Contribution of Productivity Growth

(Percent of GDP Growth)

Fig. 1. Relative importance of productivity growth.

of productivity growth and income growth is an indication that productivity growth is not driven by an exogenous process.

This section studies the determinants of growth by regressing per capita growth on a set of relevant variables in the sample of Latin American countries. Although this approach is silent with respect to the underlying model, it is very helpful in highlighting the main factors affecting growth. There are, however, problems. The estimation of semi-reduced forms, in general, presents problems of endogeneity that are difficult to overcome. Also, some variables may be capturing spurious correlations rather than economic relationships.

Growth equations are estimated using panel data. As may be seen from the results of the previous section, the frequency of the data is important. This is particularly relevant when using variables that may affect output through different channels in the long and the short run. By using six-year periods, it is implicitly assumed that short-run effects of the determinants of growth average out within this period.

The list of variables included in the growth equations has to be as exhaustive as possible to avoid omitted variable biases that can lead to


incorrect conclusions. As will be made clear later, whether a variable is included or not may have dramatic effects on the statistical significance of other variables.” Of course, availability and quality of data dictate the range of choice. The strategy followed here is to have at least one proxy for each of the main determinants of growth. Variables linked to capita1 accumulation (human and physical), the macroeconomic environment, government spending, degree of openness, the terms of trade, income distribution and political variables are all explored.”

The econometric technique employed can be discussed briefly by writing the equation to be estimated as follows:

Yit = cli + PXir + Uir>

where i denotes a country and t a time period (six-year average). a, is a country-specific parameter. y represents the rate of growth of per capita GDP while x is a matrix of the explanatory variables. A key issue in the use of pane1 data is how the country-specific effect is treated and consequently how the parameters should be estimated.12 An important fact to bear in mind is that some variables are time-invariant (e.g., initial GDP per capita), or there is only one observation for the entire sample period (e.g., income distribution), while other variables are available as regular time series.

There are basically two ways of estimating eq. (6). The ‘fixed effects’ method consists of running an OLS regression of yi, on xit and country dummies. This procedure is also called Least Square Dummy Variables (LSDV) or ‘within groups’ estimation. This approach has several problems, but the most important one for the purpose of this paper is that it precludes the inclusion of variables that are time-invariant for each country, since they are perfectly collinear with the dummies.

The other approach, which circumvents the problems of fixed effects, is known as ‘random effects’. This method considers each cli as a random variable and hence its stochastic component can be included in the error term of the regression. Since cli is common for all of the time series of a given country, the covariance matrix of the residuals in regression (6) is no longer diagonal. Hence, the equation has to be estimated by Generalized Least Squares (GLS). Random effects allow the inclusion of variables that are time- invariant. The main disadvantage of this approach is that the unobserved effect may be correlated with the regressors. Since it is important to estimate the effects of variables that are time-invariant, the analysis that follows uses

“Levine and Renelt (1990) perform a detailed exploration of the sensitivity of cross-country regressions of growth and conclude that most of the results from recent studies are not robust.

“These are variables for which results are presented. The effects of including variables for output composition and demographics were also explored, but with inconclusive results.

“See, for example, Hausman and Taylor (1981). Hsiao (1986) and Greene (1990).


the random effects procedure. To check that the residuals are in fact uncorrelated with the regressors, Hausman specification tests were performed and in most of the cases the hypothesis of no specification error could not be rejected at standard significance levels. Therefore, GLS would provide consistent estimates.

A description of the data is given in the appendix. The sample period considered is from 1950 to 1985. This period was subdivided into live six- year periods (1951-1956, 1957-1962, 1963-1968, 1969-1974, 1975-1980) and one five-year period, 1981-1985. For variables like GDP and the terms of trade, computing the average rate of change within a period by comparing the starting and final level of the variable may be very misleading when one of these data points is ‘abnormal’. The distortion may be quite important when the data cover relatively short periods. To overcome this problem, I use the estimated coefficient on the time trend, from a regression of the log of the variable on a constant and a time trend. This procedure gives some weight to all the observations, not just to the extreme points. The rest of the variables were constructed using simple averages for the yearly observations available.

5. Growth determinants: Results

The main results are presented in table 6. l3 All the standard errors were

computed using White’s (1980) heteroskedasticity-robust procedure. The first two regressions of table 6 show the set of the ‘most robust’ determinants of growth. Private investment, foreign investment, the variance or the average rate of inflation, and the initial level of per-capita GDP appear to be the most important determinants of growth. In most of the regressions, the coefficients of these variables appeared to be statistically significant with all estimation procedures and regardless of the inclusion of other variables. Another variable that turned out to be significant in many specifications is the literacy rate, which is used as a proxy for human capital. Government spending and political instability also appeared to be negatively correlated with growth, but these results were more sensitive to the specification. The overall lit of the regressions is quite good, and the regressions explain up to 65 percent of the variability of the six-year average rates of growth.

Regressions of GDP per capita growth against the initial level of GDP per capita, show no evidence of (unconditional) convergence, that is poorer countries do not tend to grow faster than richer onesi It has been argued,

“The section also discusses the results of regressions that are not reported in table 6. For additional evidence, in particular on the use of LSDV to estimate the parameters, on the results of specitication tests, and on the case of other variables see De Gregorio (1991a).

r4Because of the lack of GDP data for some countries at the beginning of the sample period, 1959-1961 was taken as the starting period for the initial GDP per capita variable.

Tab

le

6

Gro

wth

de

term

inan

ts

(dep

ende

nt

vari

able

: G

DP

per

capi

ta

grow

th).”

Reg

ress

ions

(1)

Con

stan

t 0.

011

(2.0

8)

Inve

stm

ent

0.09

7 (3

.87)

Var

ianc

e of

-

0.22

4 in

flat

ion

(-

12.0

3)

(10-

z)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1

1)

(12)

(1

3)

(14)

0.01

0 ~

0.22

8 -0

.001

-0

.047

0.

006

0.01

1 -

0.00

2 0.

013

-0.0

44

0.01

4 0.

025

0.02

1 0.

008

(1.8

6)

(-0.

25)

(-0.

16)

( - 2

.40)

(0

.35)

(1

.65)

(-

0.19

) (1

.61)

(-

1.91

) (2

.81)

(4

.02)

(4

.09)

(0

.56)

0.10

5 0.

086

0.07

8 0.

145

0.10

0 0.

104

0.09

0 0.

105

0.11

7 0.

103

(4.0

7)

(3.0

1)

(2.9

3)

(3.2

9)

(3.6

1)

(3.6

1)

(4.0

0)

(5.4

5)

(6.0

4)

(3.0

6)

-0.2

21

-0.2

21

- 0.

230

- 0.

208

~ 0.

236

~ 0.

224

- 0.

205

(-

12.3

6)

(-9.

80)

(-15

.05)

(-

10.1

6)

(-

12.3

1)

(-

12.2

8)

(-

10.9

2)

Ave

rage

in

flat

ion

(10-

2)

Fore

ign

inve

stm

ent

0.55

5 (4

.34)

-0.1

48

-0.4

14

-0.8

01”

- 1.

023b

( ~

11.

88)

(-

12.3

0)

(-4.

10)

(-4.

16)

~ 0.

687

( - 3

.03)

0.48

5 0.

617

0.57

5 0.

369

0.53

1 0.

183

(2.2

1)

(4.2

1)

(4.6

0)

(2.1

7)

(4.2

0)

(1.0

1)

0.04

3 0.

107

0.02

1 (2

.56)

(3

.74)

(1

.65)

- 0.

027

-0.1

39

-0.3

50

(-7.

41)

0.52

1 (4

.04)

0.

600

0.32

8 (4

.28)

(4

.67)

0.02

1 0.

021

(1.5

7)

(1.6

5)

0.32

8 (2

.38)

0.06

5 (2

.35)

-0.1

07

( - 1

.89)

- 0.

963

( - 4

.89)

0.19

3 0.

413

(1.1

6)

(2.7

3)

0.02

8 (1

.36)

- 0.

053

(-

1.61

)

-0.7

34

-0.8

87

(-8.

11)

(-6.

32)

2 L

itera

cy

(10-

Z)

Gov

ernm

ent

spen

ding

Initi

al

GD

P -

0.72

6 (1

0-s)

(-

6.81

)

Ter

ms

of

trad

e

Polit

ical

-

0.24

2 va

riab

le

(-

1.81

) (1

0-q

assa

ss.

liber

t. N

umbe

r of

ob

serv

a-

tions

65

R2

0.57

0.02

9 (0

.32)

- 0.

645

(-1.

81)

( -

0.65

)

-0.7

64

- 0.

954

-0.7

44

(-3.

11)

(-6.

42)

( - 5

.45)

0.01

3 (0

.18)

-2.1

8)

- 1.

218

-0.7

39

-0.7

19

- 6.

39)

( - 7

.68)

(-

7.

93)

-0.7

08

- 0.

807

- 0.

823

(-6.

11)

( - 7

.03)

(-

7.81

)

- 0.

386

-0.3

03

(-3.

14)

(-3.

31)

civi

l po

lit.

righ

ts

righ

ts

-0.1

93

(-

1.35

) po

lit.

65

65

65

64

68

58

65

65

64

65

65

65

65

0.54

0.

58

0.61

0.

46

0.22

0.

56

0.42

0.

49

0.36

0.

62

0.58

0.

67

0.57

?-st

atis

tics

in

pare

nthe

ses.

A

ll re

gres

sion

s w

ere

estim

ated

us

ing

pane

l da

ta

with

ra

ndom

ef

fect

s an

d W

hite

’s

robu

st

corr

ectio

n fo

r st

anda

rd

erro

rs.

bLog

arith

m

of

aver

age

infl

atio

n.


however, that after controlling for variables that explain cross-country differences in the rate of technological progress, rates of output growth should converge [e.g. Barre and Sala-i-Martin (1990)]. The regressions in table 6 confirm this hypothesis for the sample used in this paper. The rest of this section describes with greater detail the results obtained for the main variables considered as growth determinants.

5.1. Foreign investment

The results show that, on average, foreign investment is three to six times more efficient than total investment. l5 This result should not be too surprising since total investment data includes several kinds of investment with large quality differentials.16

Besides productivity differentials, the wide gap between total and foreign investment may be explained by other arguments. For most of the 195&1985 period, Latin American countries lacked full access to international capital markets to linance investment. To the extent that these borrowing constraints are likely to be correlated with foreign investment, the high coefficient on foreign investment may also be capturing the effect of the availability of capital inflows. Therefore, the results not only reflect that productivity of foreign investment is higher than that of domestic investment, but may also show the positive effect of capital inflows on growth.

5.2. Inflation

The anti-growth effects of inflation and its variance appear to be very important in all specifications. To check whether the results are dependent on the sample, and in particular whether they are driven by the inclusion of countries experiencing high inflation, the sample was altered according to two criteria. The first was to eliminate countries that experienced high inflation. Regressions similar to 1 to 4 were run sequentially, first excluding Brazil, then Argentina, then Bolivia, and finally Chile. In most of the regressions the negative and significant impact of inflation and its variance on growth was maintained. The second procedure was to eliminate all

r50nly regressions where political variables are included show a smaller role for foreign investment. However, even when the estimates are not signilicantly different from zero, the point estimates are still larger than that of total investment. See, for example, regressions 12 and 13. In addition, to control for possible endogeneity of foreign investment, growth regressions using total foreign investment in Latin America (in 1980 U.S. dollars) as an instrument were estimated. Results are similar to those of table 6, suggesting that there are no serious problems of endogeneity of foreign investment.

16An analogous result is obtained by De Long and Summers (1991) for investment in equipment.


observations with inflation rates higher than a specified cutoff point. Inflation rates of 50, 40, 30, 20 and 10 percent per year were used as cutoff points. Again, the results were found to be robust.

Although average inflation and inflation variance have a negative effect on growth, providing an estimate of their quantitative effect is difficult because the value of the parameter is highly dependent on the sample. This feature suggests the existence of a nonlinear relationship between inflation and growth. In view of this possibility, the level of inflation was replaced by its logarithm in specification 2 (see regression 5). The coefficient on inflation can then be interpreted as a semi-elasticity and is robust to changes in the sample. Regression 5, which in addition includes government spending, shows an inflation coefficient of about 0.008, which implies that decreasing the average rate of inflation in the sample (excluding the Bolivian hyperinflation) from 34 percent to 17 percent would increase per capita growth by 0.4 percent per annum.”

Notice, however, that the total effect of inflation on growth should include the impact of inflation on investment. A simple way to estimate this effect, without estimating investment equations, is to exclude all forms of investment from the growth equations. The semi-elasticity of growth with respect to inflation rises to 0.01 (see regression 6). This result indicates that inflation exerts a negative effect on growth primarily through the productivity

of capital rather than through its rate of accumulation. An endogenous growth model to illustrate the relation between inflation

and growth is presented in De Gregorio (1991b). In that model, inflation is considered to be the result of an inefficient tax system. The model discusses the role of money in firms’ operations and its effect on the investment rate. When money is required to buy capital goods, inflation is similar to a tax on investment. The model also shows how inflation can affect the productivity of capital. Inflation induces people and firms to divert resources from productive activities to other activities that allow them to reduce the burden of the inflation tax. The resulting reductions of employment in goods producing sectors that are subject to scale economies may reduce the rate of investment and its productivity. For example, high inflation may lead to excessive resources being devoted to cash and portfolio management, to avoid the costs of inflation, which otherwise could be used to promote endogenous growth through such activities as R&D and human capital accumulation.

Although the above result seems to contradict the traditional Phillips- curve relationship, it is not inconsistent with its existence. The reason is the frequency of the time series used. It is possible that inflation and output

“This result is similar to the one found by Cardoso and Fishlow (1989), although their result holds only for high inflation experiences.


growth display a positive correlation at a quarterly frequency, while at lower

frequencies (six-year observations) only a negative effect remains.

5.3. Terms of trade

The importance of the terms of trade has always been at the heart of the discussion about development in Latin America. In addition, the idea of a secular deterioration in the terms of trade served as a basis for the early proposals by the Economic Commission for Latin America and the Caribbean (ECLAC) on development through import substitution.”

The terms of trade were included in many specifications. The most common finding, such as in regression 7, was a coefficient insignificantly different from zero.” The general conclusion is that secular changes in the terms of trade have no effect on growth. Arguably, the terms of trade could affect growth indirectly by affecting the rate of investment. However, the exclusion of investment did not alter the main results.20

Latin American economies are highly vulnerable to the external environment. It is, thus, not surprising that at high frequencies the terms of trade are highly correlated with output fluctuations. What these results show is that, in the long run, this correlation disappears and it cannot be argued that improving terms of trade will induce faster growth, although income will certainly increase. A possible explanation for this result lies on the fact that many Latin American economies depend quite heavily on the export of primary commodities. An improvement in the terms of trade may be an incentive to investment and growth in the primary sector, but at the expense of other, probably more dynamic, sectors. 21 This could be characterized as a sort of ‘long-run Dutch disease’ phenomenon.

5.4. Economic openness and measures of investment in human capital

Although it is not a general proposition, it is expected that openness would affect growth positively.22 Several indicators of openness were included as explanatory variables in the growth regressions: the share of exports in GDP, the share of trade in GDP, and an index based on the

‘*See Prebisch (1984) for a historical perspective on his position. “Some regressions that included the terms of trade were the only ones where a specification

test comparing tixed versus random effects rejected the null hypothesis of no specification error. Therefore, if there are omitted variables, this result would suggest they are correlated with the terms of trade.

Wardoso (1991), when estimating investment equations, has found that investment is affected negatively by a terms of trade deterioration.

“Grossman and Helpman (1989) present a theoretical model that can generate this result. “For further discussion see, for example, Edwards (1991) and Roubini and Sala-i-Martin

(1991).


classification made by the World Bank in the World Development Report 1987.23 None of these variables was found to have significant effects on output growth.

One possible reason for these results may be that the sample is inadequate to address the question of openness and growth. Most of the growth in these Latin American countries during the sample period had taken place under the auspices of strong import substitution policies. Therefore, the more successful the anti-export bias was, the higher the growth rates were in the early stages of import substitution. Another factor influencing the result may have been the lack of variability in trade policies across the sample countries. A sustained outward orientation policy takes some time to yield benefits; in Latin America significant efforts toward openness began only in the 1970s.

School enrollment indices, primary and secondary, were also used as indicators of investment in human capital, Barro (1991) finds a positive sign for the coefficient of school enrollment in a cross section of 98 countries, while Romer (1990) finds no significant effect. In the sample used in this paper, school enrollment indices did not appear to have significant effects on growth, and in a few equations they appeared to be negatively correlated with growth. These results hold regardless of whether investment rates were included as one of the regressors. A similar result was obtained when college enrollment in engineering and sciences was used as a proxy for human capital investment.

As pointed out before, the literacy rate is the only human capital variable that has the expected sign and is signticant in some specifications. The failure of the literacy rate to show a significant coefftcient in other cases may be due to its collinearity with physical investment.24 This is confirmed in regression 10, which excludes physical investment; most of the coefficients of this regression are similar to those of regression 5, except for the coefficient of the literacy rate, which increases significantly.

5.5. Government consumption

Recent literature on the determinants of growth has focused on the role of government spending [Barro (1990) and King and Rebel0 (1990)]. These

23The index based in the World Bank classification assigns values of 4, 3, 2 or 1 to the categories of strongly outward-oriented, moderately outward-oriented, moderately inward- oriented, and strongly inward-oriented countries, respectively. The classification is reported for two periods: 1963-1973 and 1973-1985. The value assigned for 1963-1973 was used for the first four subperiods in the panel, and the value for 1973-1985 was employed for the remaining two subperiods. Learner (1988) has constructed alternative indices of outward orientation by comparing the actual patterns of trade with those predicted under no trade restrictions. This index, however, is available for only half of the sample of countries of this paper, which impedes its use.

‘% particular, it is not significant when the initial level of per capita GDP is not included.


studies have emphasized the importance of distinguishing between productive - i.e., education - and not directly productive spending, i.e., ‘government consumption’. The basic argument is that an increase in government consumption will increase the amount of distortionary taxation, and hence will reduce growth. An additional channel through which government consumption may affect growth negatively is by inducing a crowding out of private sector investment. It is recognized, however, that government spending can also be complementary to private sector investment. These hypotheses are investigated for the Latin American countries by adding government consumption in the growth regressions.

The main result can be summarized as follows: government consumption has a significant and negative effect on growth only when literacy rates are included in the equation. This can be seen, for example, in regressions 9 and 10. The result also holds when Barro and Wolfs (1989) measure of non- productive government consumption, which excludes defense and education spending, is used as a regressor.

The issue of whether the low significance of government consumption is due to a crowding out of private investment can be analyzed by comparing regressions 5 and 10. The small differences in the estimated coefficients for government spending suggest that crowding out is rather small. When private investment is excluded, the point estimate increases, in absolute value, from 0.11 to 0.14. Statistically, however, the null hypothesis that both coefftcients are equal cannot be rejected at a 10 percent significance level.

5.6. Income distribution and political variables

The roles of income distribution and political variables on growth were investigated. The share of income received by the highest 20 percent of households, the lowest 20 percent, and the lowest 40 percent of households were used as proxies for income distribution. None of the coefficients of these variables in the growth equations were significantly different from zero. Therefore, it can be concluded that there is no direct link between income distribution and growth in the sample of countries beyond the effect that income distribution may have on the other determinants of growth.25

Regressions 11 through 14 add as growth determinants political variables taken from Barro and Wolf (1989). Consistent with other studies, a higher level of civil liberties or political rights, and a lower index of assassinations were found to have a positive influence on growth.26 Note again, that these

‘sAlesina and Rodrik (1991), Perotti (1990) and Persson and Tabellini (1991) study the role of income distribution on growth in a political economy model and present empirical evidence.

Z60ther indicators, like number of strikes, number of government crises, number of constitutional changes, number of coups, revolutions and riots were found not to have a significant effect on growth. See Alesina et al. (1991) for a recent cross-country study on the relationship between political instability and growth.

78 .I. De Gregorio, Economic growth in Latin America

results control for investment. Thus, the role of political variables is not confined to their possible negative effect on investment.

A traditional explanation given to this correlation is that civil liberties affect growth through their positive influence on property rights. The argument is that undefined property rights - for example, threat of expro- priations - reduce the incentive to invest and to innovate because of the uncertain appropriability of the returns. Although the hypothesis is appeal- ing, it seems an unlikely explanation for obtaining these results in a sample of Latin American countries. A look at the political evolution of Latin America reveals that the relationship between the political variables used in this paper and the protection of property rights is at most weak. It is common to find absence of political rights in the presence of strong protection of property rights, and vice versa.

A more attractive explanation for the adverse effect that the absence of political rights and civil liberties have on growth, although without clear empirical support, is the influence that the policitical environment may have on the choice of public policy and, thus, on growth. Different political environments may set different constraints on public policy. For example, the quantity and quality of public investment, the role of the government in production, the extent of regulation, and many other factors, may be determined to a large extent by the characteristics of the political system.

The political variables are, however, not significant when the measures of inflation are introduced in logarithmic form rather than in linear form, since they are highly correlated. For example, when added to regression 5 none of the coefficients on the political variables is significantly different from zero.

5.7. An assessment of growth determinants

Most of the previous discussion has dealt with the statistical significance and expected sign of the estimated coefficients, and on the robustness of the findings. This section closes the empirical analysis by providing a means of establishing the quantitative importance of each variable in explaining actual growth performance. This is done by presenting a decomposition of the deviation of each country’s per capita growth with respect to the regional mean.

Table 7 reports the results of the decomposition exercise using the coefficients from regression 5. Although the fit of this regression was not the highest, it was found to have coefficients not affected by variations in the sample. Notice, however, that regression 5 does not include political variables. Since the regressors are highly correlated with the logarithm of inflation, the values for the contribution of inflation shown in table 7 are likely to be capturing also part of the effects of the political environment.

Tab

le

I I-

Det

erm

inan

ts

of

grow

th

perf

orm

ance

in

L

atin

A

mer

ica

(ave

rage

pe

rcen

t pe

r ye

ar).”

g B

Fo

reig

n G

over

nmen

t In

itial

G

row

th

GD

P D

evia

tion

Inve

stm

ent

inve

stm

ent

Lite

racy

co

nsum

ptio

n In

flat

ion

GD

P R

esid

ual

QQ

pe

r ca

pita

fr

om

mea

n e

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

“%

Arg

entin

a 0.

8 -0

.0

0.7

0.4

- 1.

4 -

1.3

0.1

0.8

-0.8

ff

B

oliv

ia

-0.6

-0

.1

-0.6

-0

.2

-0.6

0.

8 -0

.1

0.2

-1.4

%

Bra

zil

0.9

-0.1

-0

.5

-0.1

-0

.9

0.9

2.9

4.1

3.1

3.

Chi

le

1.7

-0.2

0.

7 -0

.6

-0.9

-0

.9

-0.5

0.

9 -0

.7

Col

ombi

a 0.

1 -0

.0

0.2

0.2

0.2

0.5

-0.5

2.

3 0.

7 j

Cos

ta

Ric

a -0

.5

0.3

0.6

-0.6

0.

8 0.

3 0.

0 2.

4 0.

8 E

cuad

or

0.9

0.2

-0.2

-0

.2

1.1

0.9

- 1.

6 2.

8 1.

2 S

Gua

tem

ala

B;’

-

1.1

0.1

-1.6

0.

5 1.

1 0.

5 -0

.2

0.9

-0.7

M

exic

o 0.

2 0.

1 0.

1 1.

1 0.

7 -0

.3

- 1.

1 2.

5 0.

9 g

Peru

-0

.2

-0.1

0.

0 -0

.0

-0.0

0.

1 0.

2 1.

5 -0

.1

Uru

guay

-0

.6

-0.2

0.

6 -0

.1

-0.4

-0

.9

0.5

0.5

-1.1

;

Ven

ezue

la

-0.8

0.

3 0.

1 0.

3 1.

1 -2

.8

-0.1

-0

.2

- 1.

8 m

T

.

“Usi

ng

coel

lkie

nts

from

re

gres

sion

5.

Col

umn

(7)

=(9)

-sum

of

(1

) to

(7

).

g


The average per capita growth in the sample is 1.6 percent.27 Each of the first six columns reports, in percentage points, the amount of the difference between each country’s growth and Latin America’s growth that is attribu- table to the deviation of each variable with respect to its regional mean. In most of the cases, investment, literacy, inflation and initial GDP are the most important determinants of growth. Government consumption and foreign investment play a less important role. The results for the relative contribution of foreign investment are interesting. Despite having a large coeffkient in the regressions, this variable is not too important in explaining variations in actual growth performance, because the level of foreign investment across the region has been modest. While average GDP share of total investment in the sample is 17 percent, the average of foreign investment is about 1 percent. The fact that the estimated coeffkients were large (for example, relative to the coeffkient of total investment) indicates, however, that its marginal contribution is high.

Literacy rates have had an important positive contribution to growth in the Southern Cone countries, while the presence of a low literacy rate has played a significant negative role in Bolivia, Brazil and Guatemala. The effect of initial GDP is large in most countries. In some cases, however, this measure may be distorted by changes in the terms of trade. The case of Venezuela is the most notable one. Since GDP data are based on 1980 prices, the initial GDP at 1950s prices is certainly lower than GDP at 1980s prices. Using 1950s prices could give a better approximation for the initial marginal product of capital and the subsequent catch-up effect.

6. Concluding remarks

The facts documented in this paper for a sample of Latin American countries confirm some of the results of previous studies on the determinants of growth. They include the role of productivity growth vis-A-vis growth of factor inputs, the positive effect of investment in physical and human capital, and the influence of the initial level of per capita income, government consumption and political variables, among other factors. The findings also question the validity of other widely-held views, such as the effects of terms of trade on growth. There are some interesting correlations that require further study, in particular the correlation among political variables, inflation and foreign investment.

*‘The average growth computed for the entire period does not coincide with the per-country average in the panel data. The discrepancies were assigned proportionally to each determinant of growth.


The results highlight the importance of the macroeconomic environment, measured by inflation, in fostering growth, at least in light of Latin American experience. The links between inflationary performance and the efficiency of the tax system may be an important channel through which macroeconomic conditions affect growth. The macroeconomic environment affects long-term growth through various other mechanisms as well. As recently suggested by Baumol (1990) and Murphy et al. (1991), the allocation of abilities across activities with different productivities is an important explanation of differences in growth performance across countries. The macroeconomic environment can thus be an important determinant of the structure of incentives that determines the allocation of talents.

It may also be useful to explore the role of other macroeconomic variables to identify channels through which macroeconomic policy influences growth (i.e., the role of the real exchange rate or the real interest rate). It seems clear, however, that macroeconomic stability is not sufficient for achieving sustained growth. For example, the debt crisis, reinforced by macroeconomic mismanagement and the subsequent stabilization attempts, shows that restoring growth goes beyond obtaining success in stabilization.28

A growing area of research deals with the interaction between the economy and the political system and with the way in which political institutions can affect growth performance. Especially important is the potential influence that the political system exerts on economic policy- making. The results of this study confirm recent evidence supporting the hypothesis that the political environment is correlated with economic variables.

Appendix: Data definitions and sources

The data are presented in the table A.1 of De Gregorio (1991a).

Time-varying data (six-year averages or sub-samples):

ECLAC yearbooks: Terms of trade.

IMF, International Financial Statistics: Exports over GDP (nominal). Imports over GDP (nominal). Foreign Investment over GDP (nominal). Average and variance of CPI inflation.

Summers and Heston (1988): GDP per capita average growth (RGDPl). Investment rates. Government consumption share on GDP.

World Bank, World Development Report 1987: Index of outward orientation.

“See Corden (1991) and Dornbusch (1991).

82 .I. De Gregorio, Economic growth in Latin America

World Bank, World Tables: Primary and secondary school enrollments ratio - gross enrollment of all ages as a percentage of children in the country’s primary and secondary school age.

Time-invariant data:

Barro and Wolf (1989): Number of assassinations per million population per year, 1965-1985 or sub-sample. Index of civil liberties (1 = highest, 7=

lowest), 1965-1980. Number of constitutional changes, 1965-1980. Number of coups per year, 196&1985. Number of government crises per year, 196&1985. Index of political rights (1 = highest, 7 = lowest), 196&1985. Number of revolutions and coups per year, 196&1985. Number of revolutions per year, 196&1985. Number of riots per year, 196&1985. Number of strikes per year, 196&1985. Ratio of net real government consumption net of spending on education and defense, 197&1985.

Summers and Heston (1988): Average real GDP 1959-1961 in millions of 1980 U.S. dollars.

World Bank, Social Indicators of Development: Percentage of private income received by the highest 20% of households, average 1967-1985 or sub- sample. Percentage of private income received by the lowest 20% and 40% of households, average 1967-1985 or sub-sample. Tertiary enrollment, science and engineering, students enrolled in science and engineering fields as a percentage of all students enrolled at third level, average 1967-1985 or sub-sample. Literacy rates, 100 minus illiteracy rate (%), where the illiteracy rate is the percentage of population 15 years of age or older who cannot, with misunderstanding, read and write, average 1967-1985 or sub-sample.

References

Alesina, A. and D. Rodrik, 1991, Distributive politics and economic growth, Mimeo. (Harvard University, Cambridge, MA).

Alesina, A., $. dzler, N. Roubini and P. Swagel, 1991, Political instability and economic growth, Mimeo. (Harvard University, Cambridge, MA).

Barro, R., 1990, Government spending in a simple model of endogenous growth, Journal of Political Economy 98, s103-~125.

Barro, R., 1991, Economic growth in a cross section of countries, Quarterly Journal of Economics 104, 407433.

Barro, R. and X. Sala-i-Martin, 1990, Economic growth and convergence across the United States, Working paper no. 3419 (NBER, Cambridge, MA).

Barro. R. and H. Wolf, 1989, Data appendix for economic growth in a cross section of countries, Mimeo. (Harvard University, Cambridge, MA).

Baumol, W., 1990, Entrepreneurship: Productive, unproductive, and destructive, Journal of Political Economy 98, 893-92 1.

Cardoso, E., 1991, Capital formation in Latin America, Working paper no. 3616 (NBER, Cambridge, MA).


Cardoso, E. and A. Fishlow, 1989, Latin America economic development: 195@1980, Working paper no. 3161 (NBER, Cambridge, MA).

Chenery, H., S. Robinson and M. Syrquin, 1986, Industrialization and growth: A comparative study (Oxford University Press, Oxford).

Corden, M., 1991, Macroeconomic policy and growth: Some lessons of experience, Proceedings of The World Bank Annual Conference on Development Economics 1990, 59-84.

De Gregorio, J., 1991a, Economic growth in Latin America, Working paper no. WP/91/71 (IMF, Washington, DC).

De Gregorio, J., 1991b, Inflation, taxation, and long run growth, Mimeo. (IMF, Washington, DC).

De Long, B. and L. Summers, 1991, Equipment investment and economic growth, Quarterly Journal of Economics 104,445-502.

Denison, E., 1985, Trends in American economic growth 1929-1982 (Brookings Institution, Washington, DC).

Dornbusch, R., 1991, Policies to move from stabilization to growth, Proceedings of The World Bank Annual Conference on Development Economics 1990, 1948.

Dornbusch, R. and Y.C. Park, 1987, Korean growth policy, Brookings Papers on Economic Activity 2, 389-444.

Edwards, S., 1991, Trade orientation, distortions and growth in developing counties, Working paper no. 3716 (NBER, Cambridge, MA).

Fischer, S., 1991, Growth, macroeconomics, and development, Working paper no. 3702 (NBER, Cambridge, MA).

Greene, E., 1990, Econometric analysis (McMillan Publishing Co., New York). Grossman, G. and E. Helpman, 1989, Growth and welfare in a small open economy, Discussion

papers in economics no. 145 (Woodrow Wilson School, Princeton University, Princeton, NJ). Hall, R., 1988, The relation between price and marginal cost in U.S. industry, Journal of

Political Economy 96, 921-947. Harberger, A. and D. Wisecarver, 1977, Private and social returns to canital in Uruauav.

Economic Development and Cultural Change 25, 41146. I _,

Hausman, J. and W. Taylor, 1981, Panel data and unobservable individual effects, Econometrica 49, 13761398.

Hsiao, C., 1986, Analysis of panel data (Cambridge University Press, Cambridge, U.K.). King, R. and S. Rebelo, 1990, Public policy and economic growth: Developing neoclassical

implications, Journal of Political Economy 98, ~126~150. Kormendi, R. and P. Meguire, 1985, Macroeconomic determinants of growth: Cross-country

evidence, Journal of Monetary Economics 16, 141-163. Learner, E., 1988, Measures of openness, in: R. Baldwin, ed., Trade policy issues and empirical

analysis (Chicago University Press, Chicago, IL). Levine, R. and D. Renelt, 1990, A sensitivity analysis of cross-country growth regressions,

Mimeo. (The World Bank, Washington, DC). Lucas, R., 1988, On the mechanics of economic development, Journal of Monetary Economics

22, 342. Maddison, A., 1987, Growth and slowdown in advanced capitalist economies, Journal of

Economic Literature 25, 649-698. Murphy, K., A. Shleifer and R. Vishny, 1991, The allocations of talents: Implications for growth,

Quarterly Journal of Economics 104, 503-530. Perotti, R., 1990, Income distribution and growth: Theory and empirical evidence, Mimeo.

(Columbia University, New York). Persson, T. and G. Tabellini, 1991, Is inequality harmful for growth?, Working paper no. 3599

(NBER, Cambridge, MA). Prebisch, R., 1984, Five stages in my thinking of development, in: G. Meier and D. Seers, eds.,

Pioneers in development (Oxford University Press, Oxford). Rebelo, S., 1991, Long run policy analysis and long run growth, Journal of Political Economy

99, 50&521. Romer, P., 1986, Increasing returns and long run growth, Journal of Political Economy 94,

1002-1037. Romer, P., 1989, Capital accumulation in the theory of long run growth, in: R. Barro, ed.,

Modern business cycle theory (Harvard University Press, Cambridge, MA).


Romer, P., 1990, Human capital and growth: Theory and evidence, Carnegie Rochester Conferences Series on Public Policy 32, 251-286.

Roubini, N. and X. Sala-i-Martin, 1991, The relationship between trade regime, financial development and economic growth, Mimeo. (Yale University, New Haven, CT).

Solow, R., 1957, Technical change and the aggregate production function, Review of Economics and Statistics 39, 312-320.

Summers, R. and A. Heston, 1988, A new set of international comparisons of real product and mice levels estimates for 130 countries, 195Gl985, Review of Income and Wealth 34, l-25.

Summers, R. and A. Heston, 1991, The Penn world table (Mark 5): An expanded set of international comparisons, 195Gl988, Quarterly Journal of Economics 104, 327-368.

White, H., 1980, A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica 48, 817-838.

Economic growth in Latin America

Documents

Transcript of Economic growth in Latin America