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The authors wish to thank Carlos Végh, Henry Chappell, and three anonymous referees for very helpful comments. Francisco Veiga also wishes to express his gratitude for the financial support of the Portuguese Foundation for Science and Technology (FCT), under research grant POCTI/32491/ECO/2000 (partially funded by Fondo Europeo de Desarrollo Regional (FEDER)), and acknowledge the research assistance of Helena Fernandes. The corresponding author is Francisco José Veiga, Departamento de Economia, Escola de Economia e Gestão, Universidade do Minho, P-4710-057 Braga, Portugal. Tel.: +351-253604534; Fax: +351-253676375; E-mail: firstname.lastname@example.org.
Three additional shortcomings of the analysis in Cukierman, Edwards, and Tabellini (1992) are the presence of endogeneity in some explanatory variables, the absence of explanatory variables accounting for inflation inertia, and the use of a cross-sectional dataset using averages from 1971 to 1982 for only 79 countries. We use System-GMM estimation applied to dynamic panel data covering the period 1960–99 with annual data for around 100 countries. This methodology allows us to fully address the above-mentioned shortcomings and to use additional information provided by the changes of the different variables over time to account for the developments in inflation in each country.
Missing values for some variables reduce the number of countries to at most 97 in the estimations for inflation and to 66 in those for seigniorage.
On this database, see Beck and others (2001). Available on the Internet through Philip Keefer’s page in the World Bank’s site (http://www.worldbank.org/research/bios/pkeefer.htm).
Available on the Internet (http://www.freetheworld.com/release.html). This report presents data on the index of economic freedom and its components for the years 1970, 1975, 1980, 1985, 1990, 1995, and 2000. In order to avoid a great number of missing values in our sample, straight-line interpolation was used to generate annual data.
Available on the Internet (http://www.worldbank.org/research/growth/GDNdata.htm).
Since cabinet changes, government crises, growth of real GDP per capita, and real overvaluation can be affected by inflation, they were treated as endogenous. As done for lagged inflation, their lagged values two and three periods were used as instruments in the first difference equations and their once lagged first differences were used as instruments in the levels equation.
These correlations are, respectively, 51 percent, -56 percent, and 40 percent. The complete correlation matrix is available from the authors upon request.
Several changes in results occur: government crises becomes more significant; the polity scale changes sign and becomes statistically significant; agriculture (in percent of GDP) changes sign; trade (in percent of GDP) becomes statistically significant; and the U.S. treasury bill rate, becomes more significant.
Thus, if the inflation rate is at its sample mean of 51.98 percent, a government crisis will push it to 60.35 percent, that is, the inflation rate will increase by 8.37 percentage points.
A series of robustness tests not shown here were also performed. These consisted in adding more variables to the model of Column 3 of Table 1 or in replacing some variables for reasonable alternatives. We found that the following changes lead to lower inflation: greater executive constraints, more political rights, and more civil liberties (when each of these variables replaces the polity scale); and higher real GDP growth (when used instead of growth of real GDP per capita). Proxies for ideological polarization, urbanization, currency inside banks, GDP growth of main trading partners, the exchange rate regime, and central bank independence, were not statistically significant. We also estimated models in which a dummy variable for each year/decade or region was included in order to control for timespecific or region specific effects. Then, we performed estimations for alternative samples: first, excluding extreme values of inflation (annual rates above 1000 percent); and, then, excluding Latin America. Results (available from the authors upon request) were very similar to those shown in Table 1.
Similar results are obtained when the Index of Political Cohesion (DPI, variable Ipcoh), or the fractionalization ratio (DPI, variable Frac) are used instead of government crises or cabinet changes.
We also investigated the main determinants of inflation volatility in a series of estimations whose results are not shown here, but are available from the authors upon request. We performed several within groups (fixed effects) estimations for a panel of the logarithm of standard deviations of inflation for three-year periods. The main findings were that inflation becomes more volatile at higher levels and that greater political instability, less economic freedom, greater ideological polarization, and greater fragmentation of the parties’ shares in parliament lead to higher inflation volatility.
Results are practically the same when we define seigniorage as the ratio of the change in reserve money (IFS, line 14) to nominal GDP (IFS, line 99b). Results are available from the authors upon request.
Hausmann tests indicate that the fixed effects specification is preferable to a random effects model and to a simple OLS model.
When included, the index of economic freedom is highly statistically significant and has a negative sign, as expected, indicating that greater economic freedom leads to lower seigniorage. Robustness tests such as those performed for inflation were conducted, and results remained essentially the same.