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The authors are particularly grateful to Bruce Hansen for very helpful discussions and advice on the econometric issues. They also thank Paul Cashin, William Easterly, Stanley Fischer, Robert Flood, John McDermott, Peter Montiel, Ratna Sahay, Xavier Sala-i-Martin, two anonymous referees, and a number of colleagues in the IMF Institute for extremely useful comments.
See Barro (1991), Fischer (1983, 1993)>1998), and Sbordone and Kuttner (1994). This link between low inflation and high growth has also been found by various regional studies, for example, by De Gregorio (1992) for Latin America, Hadjimichael, Ghura, and others (1995) for sub-Saharan Africa, and Fischer, Sahay, and Vegh (1996) for transition economies.
It has been argued that what matters for efficient resource allocation is not so much the level of inflation but its variance. However, to the extent that the variance of inflation is positively related to its level– see Bulkley (1984), Ball (1992), Grier and Perry (1996), and Ma (1998)—the latter does affect resource allocation. While theory seems to suggest that the variability of inflation should affect growth more than its level, empirical studies show the opposite result (see Fischer 1993).
The use of the log transformation obviously requires handling negative inflation observations (221 observations for annual data and only 9 observations for five-year averaged data). In the next section, a hybrid linear-log function that allows for negative inflation rates will be discussed.
The growth rate of a variable x is computed as the first difference of log(x).
We are grateful to the referee for suggesting this particular approach.
Continuity of the relationship given by equation (1). is desirable, otherwise small changes in the inflation rate around the threshold level will yield different impacts on growth depending on whether inflation is increasing or decreasing.
The initial income variable ly0 is computed as the five-year average of real income per capita in PPP terms for the previous five-year period, allowing the identification of ly0 under fixed effects.
For industrial countries the upper bound has been set to 30 percent.
Since the threshold estimate occurs at the lower bound of the search range for developed countries (corner solution), the question is whether the minimum is at 1 percent or less than 1 percent. This question cannot be answered with five-year-averaged observations as there are only 12 observations with an inflation rate below 1 percent for industrial countries. However, this question will be re-examined in the next section with yearly data which provide more observations with low inflation.
For a more detailed discussion on the computation of the bootstrap distribution of LR0, see Hansen (1999).
Roubini and Sala-i-Martin (1995) and Cukierman, Edwards, and Tabellini (1992) have developed models that yield results along these lines. We are grateful to Paul Cashin and John McDermott for bringing this possible explanation to our attention.
A negative coefficient on ly0 implies that countries with initially low income per capita tend to grow faster than countries with higher income per capita.
The shaded areas in Table 3 indicate inflation rates that are above the threshold level of inflation.
In Table 5, the elimination of observations with inflation rates above 40 percent restricts the grid over which the search for threshold effects can be conducted.
There is a small difference in the specification of equation (1) reported in Tables 2 and 6. In Table 2, equation (1) has the five-year standard deviation of terms of trade as an explanatory variable, whereas in Table 6, it is replaced by the growth rate of terms of trade since the standard deviation cannot be computed for yearly data. If both variables are included in equation (1)., when estimated with five-year-averaged data, both become insignificant.
The data on enrollment were taken from the World Bank’s Global Development Network Growth Database maintained by William Easterly and Hairong Yu.
Data for these variables were taken from the IMF’s International Financial Statistics (IFS). Claims on the nonfinancial private sector is IFS line 32d and domestic credit is IFS lines 32a to 32f excluding 32e.
As mentioned earlier, the period is shorter for a large number of developing countries.
The confidence regions for the threshold estimates with yearly data are wider, reflecting the noisier nature of yearly data versus the five-year-averaged data.
Fischer (1993) examines the effects of inflation on investment, employment, and total factor productivity.
The estimation method used here has not been extended to standard econometric methods of handling simultaneity.