The basic neoclassical model of Solow (1956) and Swan (1956) has been the workhorse of economic growth theorists for the past three and a half decades. Its simple assumptions and structure—a single homogenous good, a well-behaved neoclassical production function, exogenous labor-augmenting technical progress, full employment, and exogenous Labor force growth—provide an elegant solution to the “knife-edge” problem posed by Harrod (1939) and Domar (1946) and ensure the attainment of a balanced equilibrium growth path (Hacche (1979)).
The Solow-Swan growth model predicts that in steady-state equilibrium the level of per capita income will be determined by the prevailing technology, as embodied in the production function, and by the rates of saving, population growth, and technical progress, all three of which are assumed exogenous. Since these rates differ across countries, the Solow-Swan model yields testable predictions about how differing saving rates and population growth rates, for example, might affect different countries’ steady-state levels of per capita income: other things being equal, countries that have higher saving rates tend to have higher levels of per capita income, and countries with higher population growth rates tend to have lower levels of per capita income.
Recently, the Solow-Swan model has come under attack by the new growth theorists, who dismiss it in favor of “endogenous growth” models that assume constant or increasing returns to capital. These critics allege that the standard neoclassical model fails to explain observed differences in per capita income across countries. The different implications of the two growth models have led to renewed empirical work in recent years. A major concern of this work has been whether one should see a long-run tendency toward convergence of per capita income levels across countries. “Unconditional convergence” implies that in a cross-country sample the simple correlation between the growth rate of real per capita GDP and the initial level of real per capita GDP is negative. In other words, the lower the starting level of real per capita income, the higher is its subsequent rate of growth. In a recent cross-section study, however, Barro and Sala-i-Martin (1992) find that this simple correlation is positive rather than negative, albeit statistically insignificant.
In itself, the empirical evidence against unconditional convergence is not inconsistent with the implications of the neoclassical growth model. The Solow-Swan model does not predict unconditional convergence of per capita income across countries; rather, it predicts convergence only after controlling for the determinants of the steady state (that is, it predicts “conditional convergence”). Recent work by Mankiw, Romer, and Weil (1992) contends, using a cross-sectional approach, that the Solow-Swan model’s predictions are indeed consistent with the empirical evidence. They also find, however, that if human capital is not accounted for in the model the quantitative implications of different saving and population growth rates are biased upward (in absolute value), since human capital is positively correlated with both saving and population growth.
Accordingly, in an effort to understand the quantitative relationships among saving, population growth, and income, Mankiw, Romer, and Weil augment the Solow-Swan model to include human capital accumulation. They find that this variable is indeed correlated positively with saving and population growth. Relative to estimates based on the textbook model, this augmented Solow-Swan model implies smaller effects of saving and population growth on per capita income growth and explains about 80 percent of the cross-country variation in per capita income.
Despite the evidence on the failure of per capita income to converge across countries—the failure of the “unconditional convergence” hypothesis—Mankiw, Romer, and Weil find evidence of conditional convergence at about the rate predicted by the Solow-Swan model once cross-country differences in saving and population growth rates are taken into account. Moreover, they interpret the available evidence on crosscountry variations in the rates of return to capital as being consistent with the Solow-Swan growth model. Thus, their work provides empirical support for the model and casts doubt on the new endogenous growth models that invoke constant or increasing returns to capital.
This paper extends the Mankiw, Romer, and Well analysis in two directions. First, a panel of time series cross-sectional data is used to determine the significance of country-specific effects that are assumed away in the cross-sectional approach employed by Barro and Sala-i-Martin (1992) and Mankiw, Romer, and Weil (1992), as well as nearly all other studies. In order to exploit the additional information contained in these panel data, we extend the econometric analysis by applying an estimation procedure outlined in Chamberlain (1984). Second, we assume that labor-augmenting technical change is influenced by two potentially important factors: (1) the extent to which a country’ s trade policies are outward-oriented—whether they increase or decrease its openness to international trade (see Edwards (1992)); and (2) the stock of public infrastructure in the domestic economy.
As already noted, our first extension of the Mankiw, Romer, and Weil analysis refers to the econometric treatment of the data. In the empirical part of their paper, they use cross-sectional data for various groups of countries. Essentially, they take averages of the relevant variables over the whole period, 1960-85. Since only one cross-section of countries is used for the entire time period, they are obliged to make some restrictive assumptions about the nature of the shift parameter (technology) in the neoclassical production function and its relation to other variables. Specifically, all unobservable factors that characterize each economy (and are contained in the shift parameter) are assumed to be uncorrelated with the available information. Econometrically, this means that “country-specific” effects are ruled out by assumption. Our procedure, on the other hand, allows for a more general econometric specification of the model by appropriately using panel data to account for important country-specific effects. This approach yields a number of interesting extensions to the empirical results of Mankiw, Romer, and Weil, particularly when the estimates for the full sample of both industrial and developing countries are compared with those for developing countries only. 1 Provided the assumptions required for using the panel data hold, our approach also improves the efficiency of the estimates by using more information.2
Our second, related, extension of Mankiw, Romer, and Weil’ s empirical analysis refers to the country-specific variables—trade policies, human capital, and government fixed investment—that we include in the model. Policies that foster more openness in a country’ s international trade regime help to stimulate labor-augmenting technological change in two ways.3 First, the import-export sector serves as a vehicle for technology transfer through the importation of technologically advanced capital goods, as elucidated by Bardhan and Lewis (1970), Chen (1979), and Khang (1987), and as a channel for intersectoral external economies through the development of efficient and internationally competitive management, the training of skilled workers, and the spillover consequences of scale expansion (Keesing (1967) and Feder (1983)). Second, rising exports help to relieve the foreign exchange constraint—that is, a country’ s ability to import technologically superior capital goods is augmented directly by rising export receipts and indirectly by the higher flows of foreign credits and direct investment caused by the country’ s increased ability to service debt and equity held by foreigners. 4
As regards government fixed investment, it is reasonable to assume that an expansion in the amount of public goods concentrated in physical infrastructure (such as transport and telecommunications) will be associated with greater economic efficiency. Empirical studies that emphasize other productive public expenditures, such as education and health spending, include Diamond (1989), Otani and Villanueva (1990), and Barro (1991); those that focus on fixed investment include Diamond (1989), Orsmond (1990), and Barro (1991).
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Malcolm Knight, Senior Adviser in the Middle Eastern Department, holds a doctorate from the London School of Economics and Political Science.
Norman Loayza, a doctoral student at Harvard University, was a Summer Intern in the Developing Country Studies Division when this study was prepared.
Delano Villanueva, Assistant to the Director, Middle Eastern Department, received his Ph. D. from the University of Wisconsin.
The authors wish to thank Professors Zvi Griliches and Gary Chamberlain for helpful advice on the econometric methodology, Jong-Wha Lee for providing the data on tariffs, Jose De Gregorio, Graham Hacche, Manmohan Kumar, and Julio Santaella for valuable comments, and Ravina Malkani for excellent research assistance.
Our sample of industrial countries consists of the 21 developed countries that are members of the OECD; our sample of developing countries consists of 76 non-OECD developing countries. See the appendix.
The panel data set increases the number of observations from 98 to 490—that is, 98 countries multiplied by five time periods of five years each.
The transfer of efficient technologies and the availability of foreign exchange have featured prominently in recent experiences of rapid economic growth (Thirlwall (1979)).
Note that as to goes to infinity, both sides of equation (9) go to the value rg. This is so because in the limit (steady state), the growth of per capita output is equal to g, the exogenous growth rate of technology.
This refers to those data in our study for which only cross-sectional data are available.
This assumption corresponds to that in Mankiw, Romer, and Weil. The value for g + δ that is used in the estimation procedure actually matched the available data.
In the term Zt-1, the index “1” refers to five years.
It is well known that standard growth models do not account for growth in economies that specialize in the extraction of depletable resources (see Sala-i-Martin (1990)).
Note that in the estimating equation (9) the parameters η and r appear on both sides; we set r = 5 years in our panel data regressions.
The half-life formula is T = ln(2)/η, where T is number of years.
Somehow contradicting our finding that the capital share in production is approximately the same for both industrial and developing countries. De Grego-rio (1992) finds an estimate for the capital share of about 0.5 for a sample of Latin American economies.
There are two basic reasons for this imperfection. The first has to do with the fact that we are grouping together all unobservable factors into the country-specific effects; thus, if an unobserved variable affects the two samples differently, we obtain sharper estimates by separating the two samples. The second reason may be the presence of nonlinear interactions betw:een physical capital investment and the variables that are left out of the first model, such as education, public infrastructure, and openness to trade (linear interactions are accounted for by our methodology).