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Macroeconomic Determinants of Growth: SomeImplications of DisaggregationKanhaya L. Gupta aa University of Alberta , Edmonton, CanadaAlta T6G 2E1Published online: 28 Jul 2006.

To cite this article: Kanhaya L. Gupta (1988) Macroeconomic Determinants of Growth: Some Implications of Disaggregation,Applied Economics, 20:6, 843-852, DOI: 10.1080/00036848800000075

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Applied Economics, 1988, 20, 843-852

Macroeconomic determinants of growth: some implications of disaggregation

KANHAYA L. G U P T A

University of Alberta, Edmonton, Alta T6G ZEI, Canada

1. I N T R O D U C T I O N

The literature on 'Sources of Growth' attempts to account for observed growth in terms of observed growth in factor inputs. Although a number of refinements have been made in the measurement of inputs, the fundamentals of this approach have remained intact over the last two decades or so'. This approach, while useful in tracking down the historical record, is less illuminating in examining the forces which cause economic growth. A somewhat different approach has recently been used by Kormendi and Meguire (1985) (henceforth K-M).' They have tried to explain the growth experience in terms of a number of hypotheses which have recently been proposed in macroeconomic literature. Using a single equation model and cross-section data for 47 countries, their results show that a substantial proportion of observed growth can be explained in terms of various policy-oriented variables. The approach, admittedly an ad hoc one, is a good first approximation as a starting point to understand the factors which cause economic growth.

In this paper it is argued that since K-M's sample includes both developed and developing countries, a disaggregation of their sample into two groups is necessary because, as it turns out, the two groups come from different populations, so that there are fundamental differences in the determinants of growth in the two groups. Further, as the results presented here show, this disaggregation also resolves some of the puzzles reported by them. At the same time, the results point to some even more interesting puzzles. The policy implications of these results for the two groups are quite different. At the same time, some of the hypotheses tested by them turn out to be even more robust than suggested by their aggregate results.

The scheme of the paper is as follows. In Section 11, the K-M model is specified. The disaggregated results and their discussion are presented in Section 111. The final section concludes with a brief summary and some suggestions for further research.

11. T H E M O D E L

Very briefly, they specify a single equation model as follows; - + + - 0 + ?

MDY = f(YPC, MDPOP, SDY, SRM, MDM, MDGX, MDEXX, MDINF). (1)

'See, for example, Denison (1962, 1979), Jorgenson and Griliches (1967), and Solow (1957), among others. 'Kormendi and Meguire (1985).

0003-6846/88 $03.00 + . l 2 0 1988 Chapman and Hall Ltd. 843

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844 K. L. Gupta

+ k This model is augmented alternately, first by the inclusion of MIX and then by CLD. The

definitions of the variables are as follows:

MDY: mean annual rate of growth YPC: 'initial' per capita income MDPOP: mean population growth rate SDY: S. D. of real output growth SRM: S.D. of money supply shocks MDM: mean of money supply growth MDGX: mean growth of the ratio of government spending to output MDEXX: mean growth of exports as a proportion of output MDINF: mean growth in the rate of inflation MIX: mean investment-income ratio CLD: civil liberty.

The expected effects of the variables are given above each one of them. Since a detailed rationale for the inclusion of these variables is given in their paper, only a brief description is provided here. Initial per capita income (YPC) and population growth (MDPOP) are assumed to affect MDY in the spirit of neoclassical theory. SDY is used as an index of the risk of aggregate technology and tests Black's (1979) hypothesis 'that countries encounter a positive risk return trade-off in the choice of aggregate technology' (K-M (1985) p. 143). SRM tests Barro's (1976,1986) hypothesis how monetary variability, by affecting real rates of return negatively, affects savings and investment, and thereby, economic growth adversely. The inclusion of MDM tests the well-known hypothesis, due to Lucas (1972,1973)and Barro (1976, 1980) among others, that anticipated changes in money supply do not affect output growth. The use of MDGX is meant to test the supply siders' assertion that increases in taxes necessitated by large government expenditures, by distorting economic incentives, adversely affect output growth. There is vast literature on the effects of 'openness' of an economy on economic growth and MDEXX is meant to capture that effect. The Tobin -Mundell effect of anticipated inflation is reflected by MDINF. MIX captures the effect of capital formation according to the neoclassical theory. Finally, the role of civil liberties is somewhat ambiguous since the proponents of market economies claim a positive influence on growth whereas those for planned economies hold the opposite view. The issue thus becomes an empirical one.

The details of the data used are given in the K-M paper. Suffice to say here that except for CLD, the rest of the data were derived from International Financial Statistics, IMF, various issues, and consisted of cross-section observations, calculated from time-series data for the period 1950-77. The data on CLD were collected from Gastil [1979].

Probably the most important estimation problem while using cross-section data is the possible presence of heteroskedastic disturbances. It is well known that in the presence of such a situation, we cannot draw proper statistical inferences. Kormendi and Meguire did not pay any attention to this issue and estimated all their equations using the OLS estimator. But, of course, it is known that if the disturbances are heteroskedastic, the OLS estimator would yield inconsistent covariance matrix estimates with the consequence mentioned above. In order to deal with this problem, White (1980) has proposed a procedure which yields parameter covariance matrix estimator which is consistent even in the presence of heteroskedastic disturbances, thus enabling us to draw correct statistical inferences and

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Macroeconomic determinants of growth 845

calculate correct confidence intervalse3 The equations reported in the next section were estimated using both estimators. The estimated regression coefficients by the two estimators were very similar, except that the White estimator generally yielded lower S. E.S of the estimated coefficients. To conserve space, only the results using White's estimator are given, but the OLS estimates are available upon request.

111. T H E RESULTS

Instead of reproducing their major conclusions, the results of this study are presented first, and their results can then be used to compare with the present disaggregated results. This would be more economical without any loss of significant information.

Following the classification adopted by the World Bank: K-M's sample was divided into two groups: Developed Countries (19) and the Developing Countries (28). The countries included in each group are given in the Appendix. In order to facilitate comparison with K-M's results, their sequence is followed for presenting the results of this study. Since their major discussion excludes MDM and MDGX, the effects of these two variables are also discussed.

According to K-M's Equations 2 ,3 and 4, MDGX did not have a coefficient significantly different from zero and, in fact, contrary to expectation had a positive sign. As for MDM, while it had a positive and significant effect, this effect was found to be highly sensitive to the inclusion of Brazil in the sample. For these reasons, MDGX and MDM were excluded from all of their subsequent estimates.

The results of this study, for the two groups, as far as MDGX and MDM are concerned, are given in Table 1. The estimates of Equations 1 and 3 correspond to their Equation 2. From these estimates one can see that MDGX has a coefficient which does not differ significantly from zero in either of the two groups, thus suggesting that K-M's aggregate results accurately reflect group behaviour. In both groups, the rate of growth of government spending does not have a significant effect on economic growth, thus indicating little support for the supply-side hypothesis in either group. But the disaggregated results of this study do differ in one respect from the aggregate results. K-M reported that the coefficient of MDGX while insignificant was positive, rather than negative as expected, and they emphasized this finding by suggesting that this contrary sign provided even a stronger rejection of the supply-side hypothesis.' On the other hand one finds that the coefficient is negative for the developed, and positive for the developing countries in this study. These opposite signs are interesting, because on the one hand they suggest that the aggregate result may well be dominated by the behaviour of the developing countries and on the other hand that the supply-side hypothesis may, after all, have some relevance to the developed countries. Clearly the issue needs further exploration. As for the developing countries, it is possible that a disaggregation of government expenditures into developmental and non-developmental expenditures may shed more interesting light on the relevance of the supply-side hypothesis to these countries. Be that as it may, the opposite signs on MDGX for the two groups provide the first example

'A formal test for heteroskedasticity for some of the equations indicated its presence in the OLS estimators, but not in the White estimator. 4World Tables, World Bank (1980). 'Kormendi-Meguire, op. cit. p. 148.

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846

Table 1. Results assessing the effects of MDGX and MDM*

K . L. Gupta

Variables Developed Countries Developing Countries

Constant 0.0623 0.0614 0.0472 0.0468 0.0490 (6.49) (8.17) (6.64) (6.69) (7.1 7)

YPC -0.0112 -0.0115 - 0.0058 - 0.0058 - 0.0050 (5.82) (4.92) (1.70) (1.68) (1.47)

MDPOP 1.4716 1.4909 0.5429 0.5402 0.4824 (5.60) (5.90) (2.97) (3.00) (2.62)

SDY 0.2095 0.2240 0.4910 0.4999 0.3786 (1.11) (1.15) (3.84) (4.26) (4.28)

SR M -0.3252 -0.3281 - 0.2639 - 0.2650 - 0.2002 (4.67) (4.17) (4.28) (4.16) (4.73)

MDM 0.1032 0.1049 0.0493 0.0495 0.0123 (1.74) (1.68) (1.50) (1.48) (0.62)

MDGX - 0.0532 - 0.022 1 (0.24) (0.13)

M D E X X -0.1564 -0.1445 0.1381 0.1359 0.1947 (0.88) (0.99) (2.36) (2.29) (4.38)

MDINF - 1.3850 - 1.3600 -0.7123 -0.7276 - 0.6485 (3.41) (3.80) (1.90) (2.01) (2.20)

R2 0.668 0.698 0.530 0.553 0.610 S. E. of estimate 0.0066 0.0063 0.01 11 0.0109 0.0093

* t values are given in parentheses; Column 3 excludes Burma; Column 4 includes Burma; and Column 5 includes Burma but excludes Brazil.

where disaggregation suggested in this paper leads to different policy implications, albeit not strong ones. It might be added that Burma is excluded from Equation 3 of Table 1 because there is no data on MDGX for it. Kormendi and Meguire's estimate in their Equation 2 also excluded Burma.

Turning to the effect of M D M , Equations 2, 4 and 5 in Table 1 are used. Accord- ing to K-M, the coefficient of M D M was 0.065 with a t value of 2.6 when Brazil was included and 0.03 with a t value of 1.3 when Brazil was excluded. These estimates were derived from equations which excluded M D G X . The coefficient was thus sensitive to the inclusion of Brazil, although it did remain positive and at least marginally significant even after Brazil's exclusion. Now, for the developed countries, the coefficient is positive and marginally significant. For the developing countries, on the other hand, the coefficient is once again positive and marginally significant when Brazil is included but is not significantly different from zero, though still positive, when Brazil is excluded. A number of observations can be made from these disaggregated results. First, the significant effect in the aggregate equation, when Brazil is included, is difficult to explain in view of the fact that the coefficient of M D M is not significant in either of the two groups. Presumably, it is due to aggregation bias. Second, even with the exclusion of Brazil, the aggregate results would appear to reflect essentially the behaviour of the developed countries. Finally, the results provide suggestive evidence that the neutrality proposition does not hold for the developed countries, but that it clearly does for the developing countries, once Brazil is excluded. At least the results of this study suggest that the question should be explored further for the developed countries. This conclusion is

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Macroeconomic determinants of growth 847

somewhat different from K-M's wherein they suggested that the question remained open for the entire ample.^ It may well be so, but the case for pursuing this question for the developed countries is a stronger one. Here again, then, one sees the usefulness of disaggregating their sample into the two groups proposed.

In the rest of the paper, estimates which exclude both MDGX and MDM are concentrated on. The aggregate results as well as the results for the two groups are given in Table 2. The table reports two sets of results, that is, when the model is augmented by including MIX and when it is not.

The Chow test was used to check whether there was a structural difference between the two groups. Results for both equations showed that this indeed was the case. Thus, for the non- augmented version the calculated value of F was 19.1 and for the augmented version 13.2. These values clearly reject the hypothesis of no difference at any conventional level of statistical significance. Thus disaggregation into the two groups is justified.

Turning to a discussion of the results in Table 2, the effect of the initial level of income, YPC, is first considered. In terms of the non-augmented version, the coefficient is highly significant for the developed but only marginally significant for the developing countries. The sign, however, is negative for both groups. The augmented version improves the significance for the developing countries. The aggregate results, thus, overstate the significance of YPC for the developing countries somewhat, but qualitatively the three results are comparable. It is interesting to note that the coefficient in the augmented version for the two groups is almost identical.

Table 2. Results for the total sample and the two groups for MDY"

Developed Developing Variables Total sample Countries Countries

Constant

YPC

MDPOP

SDY

SRM

MDEXX

MDINF

MIX

R2 S. E. of estimate

* t values are given in parentheses.

61bid.

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K . L. Gupta

The effect of population growth on the two groups is substantially different. While the coefficient of MDPOP is significant for the developed, it is only marginally significant for the developing countries. But even more striking is the difference in the magnitudes. For the developed countries, the effect is homogeneous of degree greater than 1 but for the developing countries, it is of less than 1. In fact the coefficient for the developed countries is more than three times that of the developing countries. Thus the aggregate results do not reflect the behaviour of either of the two groups.

The effect of S.D. of income SDY again is different for the two groups. The coefficient is only marginally significant for the developed countries in the non-augmented version but not significantly different from zero in the augmented version. For the developing countries, the coefficient is significant. This difference is not captured by the aggregate results where the coefficient is significant in both versions. Unlike the coefficient of MDPOP, the coefficients of SDY for the two groups and for the total are fairly close. The result with respect to the effect of SRM, S.D. of money supply shocks, is robust across the two groups and coincides with the finding for the total sample.

Turning to the effects of 'openness' as measured by MDEXX, the differences between the two groups are almost startling. For the developed countries, the coefficient is not significantly different from zero in both versions and further it is negative, contrary to the expected sign. On the other hand, the results for the developing countries are closer to their findings for the total sample, although even here thecoefficient is nearly three times greater in the non-augmented version.

The effects of anticipated inflation, as measured by MDINF again differ for the two groups. For the non-augmented version, the coefficient for the developed countries is significant and more than twice that for the developing countries, where the significance is marginal. Both results differ from the aggregate: for the developed in terms of the magnitude and for the developing in terms of the level of significance.

Turning to the effect of investment-income ratio, MIX, K-M stated that 'the investment- income ratio has major effects on economic growth' (p. 157). Our results for the augmented equation for the two groups shed a very different light on this issue. For the developed countries, the effect is negative, though not significant. For the developing countries, it is positive and significant and almost coincides with their result for the total sample, thus suggesting that the aggregate result merely reflects the results for the developing countries. This finding, combined with the present study's earlier results with respect to the effects of population growth and 'openness' of an economy, suggest that there are fundamental differences in the macro determinants of growth in the developed and developing countries and a single international cross-section is unable to capture these differences.'

'It should be pointed out that the results for the developed countries are not sensitive to the exclusion of M D M , which was earlier found to be marginally significant. The results including M D M for the developed countries are as follows;

MDY = 0.0587 - 0.0098YPC + 1.6362MDPOP - 0.0463SDY - 0.2756SRM (2.44) (2.51) (2.87) (0 .1 1 ) (2.39)

+ 0.1925MDM - 0.0734MDEXX - 1.6004MDINF -0 .0260MIX (1.69) (0.32) (1.94) (0.28) R' = 0.677 S. E. of estimate = 0.0066

A comparison of the augmented model's results in Table 2 for the developed countries and the above equation with M D M included shows that the two results are virtually the same.

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Macroeconomic determinants of growth 849

Following K-M, it is also interesting to look at the effects of the same variables as iq Equation 1 on M I X for the sake ofcomparison. These results, for the two groups, are given in Table 3.

The Chow test suggested (with an F value of 27.7) that the two groups came from different populations. One can see from Table 3 that there are a number of differences between the results for the two groups. First, the effect of YPC is opposite for the two groups. For the aggregate result, K-M remarked that, 'the positive effect of YPC: on the investment ratio is something of a puzzle. Under neoclassical hypotheses, countries with lower initial capital- labor ratios should have a greater real return, including greater investment, which implies a negative effect of YPC: on MIX' , (p. 153). This study's results suggest that this positive effect is essentially caused by developing countries' behaviour because the hypothesis is clearly confirmed for the developed countries where the coefficient is both negative and significant. So the question really is, why do we observe the non-neoclassical result for the developing countries?

The negative, albeit insignificant effect of MDPOP in the total sample is something of an anomaly given the fact that both groups show a positive effect. The positive effect of SDY remains robust across the groups, as is also the case with respect to the effect of MDINF. But with respect to the effects of SRM and M D E X X , the two groups display opposite behaviour. The expected negative effect of SRM on M I X is found for the developing countries but not for the developed countries. As for the effect of M D E X X , it is again opposite, but consistent with our earlier findings about its effect on MDY. Thus 'openness' doesn't seem to do much good for the developed countries, but is of considerable importance for the developing countries.

Table 3. Determinants of MIX*

Total Developed Developing Variable sample Countries Countries

Constant

YPC

MDPOP

SDY

SRM

MDEXX

MDINF

R2 S. E. estimate

* t values are in parentheses.

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8 50 K. L. Gupta

The role of 'civil liberty'

They also tested the validity of competing claims about the effect of 'civil liberty' on economic growth and investment-income ratio. No results are presented for the developed countries, because in their case the dummy variable for CLD takes a value of unity across the entire sub- sample. But for the developing countries, the results are given in Table 4.

As far as their aggregate results with respect to the effects of 'civil liberty' are concerned, K-M 'found a marginal effect on growth and a dramatic effect on investment' (p. 157). Looking at the results for the developing countries in Table 4, one can see from columns 1 and 2 that 'civil liberty' CLD has a coefficient which does not differ significantly from zero. As for the effect on MIX, a comparison of columns 3 and 4 shows that the addition of CLD does not add significantly to the explanatory powers of the equation, although the coefficient is marginally significant in column 4. The results in column 4 are also revealing. They found that CLD by itself explained 45 % of the variation in MIX whereas the equation corresponding to this study's results in column 4 explained 44 %. But in the present case, although CLD by itself is significant, it explains only 22 % of the variation in MIX, which is about 56 % of the R2 in column 4. In other words, within the developing countries' group itself, the effect of 'civil liberty' is substantially less pronounced than for the total sample.

Finally, a word about the explanatory power of the different models for the MDY for the total sample and the two groups. The results are brought together in Table 5.

It is clear that without MIX, the model explains a somewhat smaller proportion of the observed growth for the developing countries than for the developed countries. This difference is very little once one allows for the effect of MIX, which, however, is not

Table 4. Effect of civil liberty on MDY and M I X in developing countries*

Variables Effect on MDY

Constant 0.0496 0.0297 (9.94) (2.43)

C L D 0.0074 0.003 1 (1.28) (0.51)

YPC -0.0084 -0.0125 (2.82) (5.17)

MDPOP 0.5398 0.3563 (3.86) (2.14)

SDY 0.3727 0.2782 (1.45) (1.29)

SRM -0.1718 -0.1166 (3.34) (3.09)

M D E X X X 0.1756 0.1186 (3.49) (1.82)

MDINF -0.3881 -0.1579 (1.57) (0.72)

M I X 0.1619 (2.0'3

R2 0.510 0.575 S. E. of estimate 0.01 14 0.0106

Effect on M I X

0.1226 0.1599 (5.92) (22.0) 0.0270 0.0503 (1.90) (3.20) 0.0256 (2.39) 1.1331

(2.24) 0.5842

(1.06) - 0.341 1

(2.36) 0.3523

(2.79) -0.1421

(2.22)

* t values are given in parentheses.

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Macroeconomic determinants of growth

Table 5. Explanatory power of the models for MDY in terms of R2*

Models Total Developed Developing sample Countries Countries

Without M I X and C L D 0.586 0.668 0.508 With M I X but without C L D 0.669 0.641 0.592 With C L D but without M I X 0.60 - 0.510 With C L D and M I X 0.66 0.575

*Source: Tables 2, 4 and K-M.

surprising given this study's finding that MIX had a coefficient which was not significantly different from zero for the developed countries but highly significant for the developing countries.

IV. C O N C L U S I O N S

The approach to the determinants of economic growth proposed by K-M is an interesting and a promising one. But it is clear from the results of this study that there are fundamental differences in the macro-determinants of growth in the developing and the developed countries, so that instead of estimating models using a single international cross-section, a disaggregation of the type used in this paper would be more illuminating. With a larger sample, one may find that a further disaggregation of the group of developing countries, say, by geographical area or income level, would be even more revealing.

In particular, it was found that the'role of three fundamental determinants, namely, capital formation, population (labour force) growth, and exports (openness, trade liberalization) is so different in the two groups that the results based on an aggregation of the two groups into a single sample will imply wrong policy prescriptions. Needless to say, the results presented here, as K-M said about their aggregate results, are also exploratory in nature and a further refinement of the data, greater disaggregation, incorporation of additional hypotheses, allowance for the simultaneity of the relationships involved, and perhaps the development of a structural model, would shed further light.

A P P E N D I X

Developed Countries Australia Austria Belgium Canada Denmark Finland France

Developing Countries Argentina Bolivia Brazil Burma Chile Columbia Dominican Republic

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K , L. Gupta

FR of Germany Iceland Ireland Italy Japan Net herlands New Zealand Norway Sweden Switzerland United Kingdom United States

Ecuador El Salvador Greece Guatemala Honduras Israel Korea Mexico Nicaragua Paraguay Peru Philippines Portugal South Africa Spain Sri Lanka Taiwan Thailand Turkey Uruguay Venezuela

A C K N O W L E D G E M E N T S

Thanks are due to J. Amoako-Tuffour for research assistance and useful suggestions, and to the referee for helpful criticisms.

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