The Political Economy of Seigniorage
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author(s) E-Mail Address: aaisen@imf.org; fveiga@eeg.uminho.pt

While most economists agree that seigniorage is one way governments finance deficits, there is less agreement about the political, institutional, and economic reasons for relying on it. This paper investigates the main determinants of seigniorage using panel data on about 100 countries, for the period 1960-1999. Estimates show that greater political instability leads to higher seigniorage, especially in developing, less democratic, and socially polarized countries, with high inflation, low access to domestic and external debt financing and with higher turnover of central bank presidents. One important policy implication of this study is the need to develop institutions conducive to greater economic freedom as a means to lower the reliance on seigniorage financing of public deficits.

Abstract

While most economists agree that seigniorage is one way governments finance deficits, there is less agreement about the political, institutional, and economic reasons for relying on it. This paper investigates the main determinants of seigniorage using panel data on about 100 countries, for the period 1960-1999. Estimates show that greater political instability leads to higher seigniorage, especially in developing, less democratic, and socially polarized countries, with high inflation, low access to domestic and external debt financing and with higher turnover of central bank presidents. One important policy implication of this study is the need to develop institutions conducive to greater economic freedom as a means to lower the reliance on seigniorage financing of public deficits.

I. Introduction

The purpose of this paper is to identify the main determinants of cross-country and cross-time differences in seigniorage—government revenues from monopoly control over the creation of money. This is a challenge not yet satisfactorily confronted by the economics profession for three reasons. First, several political and institutional variables used as explanatory variables in earlier studies were relatively poorer measures of political instability and of the institutional environment than those available in new datasets such as the Database of Political Institutions (DPI), the Cross National Time Series Data Archive (CNTS), the Polity IV Database, and the Freedom House ratings. Second, our analysis is based on a richer and wider dataset, covering more countries and years than those used in previous studies and includes a larger variety of alternative model specifications. Third, our models are able to identify the circumstances under which the relationship between political instability and seigniorage is stronger, a central topic of our research which is virtually absent from previous empirical studies on the determinants of seigniorage.

Relying upon the theoretical literature and using a dataset covering around 100 countries for the period 1960–1999, we estimate panel data models to investigate the main economic and political determinants of seigniorage. After controlling for the countries’ economic structure and for several other variables that may affect seigniorage, we confirm Cukierman, Edwards, and Tabellini (1992) and Click (1998) finding that greater political instability leads to higher seigniorage levels.

This paper’s major contribution to the literature is the identification of the circumstances under which the above-referred relationship is stronger. That is, we find that political instability has stronger effects on seigniorage levels in higher inflation than in moderate- and low-inflation countries and also in developing than in industrial nations. In addition, this relationship is also stronger in countries with (i) higher turnover of central bank presidents (lower de facto central bank independence); (ii) higher social polarization, expressed in higher Gini coefficients; (iii) higher domestic debt levels as a percentage of GDP; and (iv) lower access to international financing (expressed in poorer creditworthiness ratings). Finally, authoritarian regimes and countries with low indexes of economic freedom exhibit stronger effects of political instability on seigniorage than democracies and economically freer countries. It is also worth mentioning that besides its effects on the relationship between political instability and seigniorage, economic freedom is by itself a major determinant of seigniorage. Empirical results show quite clearly that higher degrees of economic freedom are associated with lower levels of seigniorage.

The paper is structured as follows. A survey of the empirical and theoretical literature on the relationship between seigniorage, political instability, and institutions is presented in Section II. The dataset and the empirical models are described in Section III. Section IV presents the empirical results, and Section V concludes the paper.

II. The Political Economy of Seigniorage

Most economists acknowledge that differences in the way countries conduct their fiscal policies are behind the variability of the seigniorage levels they sustain. But this explanation leads to a much deeper and fundamental question, which is, why countries differ in the way they conduct fiscal policies (see Woo, 2003)? In particular, governments that are able to finance their expenditures through taxes or debt do not need to rely on seigniorage revenues. Several studies have explored the idea that structural features of a particular economy help determine its “taxable capacity.” Chelliah, Baas, and Kelley (1975), for example, provide evidence that countries with larger per capita nonexport income, more open to trade, and with larger mining but smaller agricultural sectors have, on average, a higher “taxable capacity” or ease of collection. This result leads to the conclusion that the countries’ ability to tax is technologically constrained by their stage of development and by the structure of their economies (e.g., size of the agricultural sector in GDP), and as tax collecting costs are high and tax evasion pervasive, countries might use seigniorage more frequently. But what if governments, independently of their countries’ economic structures, find it optimal to finance expenditures using seigniorage rather than levying other taxes (e.g., taxes on output)? The Theory of Optimal Taxation (see Phelps 1973; Végh 1989; and Aizenman 1992) rationalizes government behavior in many countries by showing that it might be optimal for governments to rely on seigniorage if other taxes are highly distortionary. According to this theory, governments optimally equate the marginal cost of the inflation tax with that of output taxes, thereby minimizing the distortions to the economy when choosing the optimal combination of taxes to finance their expenditures. Edwards and Tabellini (1991) and Cukierman, Edwards, and Tabellini (1992) fail to find evidence that this theory applies to developing countries. Click (1998) estimates a model using 90 countries, from 1971 to 1990, and finds that only 40 percent of the cross-country variation in seigniorage can be explained by the Theory of Optimal Taxation. The empirical failure of this theory to explain fully the cross-country differences in the use of seigniorage revenues motivated the use of theoretical and empirical models focusing on the role played by political and institutional variables.

Cukierman, Edwards, and Tabellini (1992) develop a theoretical model in which political instability and polarization determine the equilibrium efficiency of the tax system and the resulting combination of tax revenues and seigniorage governments use. Using a probit model to determine the likelihood of an incumbent government to remain in power, they provide evidence that higher political instability and polarization lead to higher seigniorage. In the empirical analysis of Section IV, we employ alternative and more direct measures of political instability, such as variables that count the exact number of cabinet changes or government crises taking place in a particular year. Moreover, whereas they use a dummy variable for democratic regimes, we use the Polity Scale (ranging between -10 and +10) to measure the degree of democracy in different countries.2

In line with Cukierman, Edwards, and Tabellini (1992), we conjecture that economies with weaker institutions might not be able to build efficient tax systems, which leads them to use seigniorage more frequently as a source of revenue. In the next sections, in addition to the effects of political instability on seigniorage, we also estimate the effects of institutions such as economic freedom and democracy. Besides structural variables accounting for the taxing capacity of the economy and political and institutional variables affecting the use of seigniorage financing of fiscal deficits, we also consider in line with Click (1998) variables that measure the ability governments have to finance transitory expenditures with domestic or external debt. To the extent that a government is able to finance its expenditure through debt, there is less need to rely on seigniorage.

Our main contribution to the literature is that our models not only identify the main political and economic determinants of seigniorage, but also reveal under which circumstances the effects of political instability on seigniorage are stronger. Our results, derived from simple econometric techniques, indicate that the causal effect of political instability on seigniorage is stronger in developing and high-inflation countries, and in the decades of the 1970s and 1980s. In addition, it is also stronger in socially polarized, less democratic and highly indebted countries. Finally, political instability will have greater effects on seigniorage in countries that have lower de facto central bank independence, lower economic freedom, and lower creditworthiness ratings. In our view, and to the best of our knowledge, there is no comprehensive study in the literature that analyzes fully the relationship between political instability and seigniorage. As it will become clear in the following sections, this paper is an attempt to contribute in this direction.

III. Data and the Empirical Model

The dataset is composed of annual data on political, institutional, and economic variables for the years 1960–1999. Although we have data on seigniorage for 144 countries, missing values for several explanatory variables reduce the number of countries in our estimations to a maximum of 104. The sources of political and institutional data are the CNTS, the DPI 3.0,3 the Polity IV dataset,4 Gwartney and Lawson (2002),5 and the Freedom House ratings.6 Economic data were collected from the World Bank’s World Development Indicators (WDI) and Global Development Network Growth Database (GDN),7 the International Monetary Fund’s International Financial Statistics (IFS), the Penn World Tables (PWT 6.1),8 Cukierman and Webb (1995),9 Dollar and Kraay (2002),10 and Levy-Yeyati and Sturzenegger (2003).11

In order to investigate the main political, institutional, and economic determinants of seigniorage levels across countries and time, we estimate panel data models, controlling for countries’ fixed effects. Seigniorage is defined in two alternative ways: the change in reserve money (line 14a of the IFS) divided by nominal GDP (line 99b in the IFS) and the change in reserve money (line 14a of the IFS) divided by government revenues (line 81 of the IFS). Table 1 shows the number of observations, means, and standard deviations of these seigniorage measures for all countries for which data are available.12

Table 1.

Seigniorage Across Countries

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We hypothesize that seigniorage levels depend on the following explanatory variables:

  • A set of variables representing political instability and institutions:

    • Cabinet Changes (CNTS)—a proxy for political instability—counts the number of times in a year in which a new premier is named and/or 50 percent of the cabinet posts are occupied by new ministers. A positive coefficient is expected, as greater instability should lead to greater reliance on seigniorage revenues.

    • Index of Economic Freedom (Gwartney and Lawson, 2002). Higher indexes are associated with smaller governments (Area I), stronger legal structure and security of property rights (Area II), access to sound money (Area III), greater freedom to exchange with foreigners (Area IV), and more flexible regulations of credit, labor, and business (Area V). Since these are characteristics of more advanced economies with lesser need of seigniorage financing, a negative coefficient is expected.

    • Polity Scale (Polity IV)—from strongly autocratic (-10) to strongly democratic (10). Although the economic theory is not conclusive, we anticipate that democracy is associated with lower reliance on seigniorage (negative coefficient).

  • A set of economic structural variables that reflect characteristics of the countries that may affect their capacity to control inflation:

    • Agriculture (in percent of GDP)—share of the value added of agriculture in GDP (WDI, World Bank). According to Chelliah, Baas, and Kelly (1975), a positive coefficient is expected.

    • Trade (in percent of GDP)openness to trade (WDI, World Bank). Since it is associated with larger revenues of import duties, we expect that countries more open to trade rely less on seigniorage revenues (a negative coefficient is expected).

    • Real GDP per capita (PWT 6.1)Richer countries have more efficient tax systems and thus have a lesser need for seigniorage (negative coefficient expected).

  • Variables accounting for economic performance and external shocks:

    • In percent of change in terms of trade (WDI, World Bank)Favorable evolution of terms of trade provides greater tax revenues (negative coefficient expected).

    • Growth of real GDP per capita (PWT 6.1)Larger growth rates are associated with increasing tax revenues, reducing the need for seigniorage (negative coefficient).

  • Variables accounting for fixed effects of countries and time:

    • country dummy variables; and

    • dummy variables for each decade—1960s, 1970s, 1980s, and 1990s.

Table 2 presents the descriptive statistics for the above-described dependent and independent variables and for additional/alternative explanatory variables that are used in the empirical analysis.

Table 2.

Descriptive Statistics

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IMF–IFS—International Monetary Fund–International Financial Statistics.

WB–WDI—World Bank–World Development Indicators.

CNTS—Cross National Time Series Database.

CWN—based on Cukierman and Webb (1995).

DPI—Database of Political Institutions.

Freedom H—Freedom House Rankings.

LYS—Levy-Yeyati and Sturzenegger.

WB-GDN—World Bank–Global Development Network.

PWT-6.1—Penn World Tables (Mark 6.1).

The empirical model for seigniorage levels can be summarized as follows:

Sit=αPIi,t1+Institβ1+Ecoitβ2+EcPitβ3+vi+εit,i=1,…,Nt=1,…,Ti(1)

Where S is seigniorage, PI is a proxy for political instability, Inst is a vector of institutional variables, Eco is a vector of economic structural variables, EcP is a vector of variables accounting for economic performance and external shocks, νi is the fixed effect of country i, and εit is the error term.

The proxy for political instability (PIi,t-1) is lagged one period for two reasons. First, political instability may translate into higher seigniorage only after some time. Furthermore, if a cabinet change or a government crisis occurs at the end of one year, it is very likely to lead to higher seigniorage only in the following year. Second, since from Aisen and Veiga (forthcoming) higher seigniorage leads to higher inflation, which may affect political instability, using the contemporaneous value of political instability could create simultaneity/endogeneity problems. Taking the first lag avoids these problems as current seigniorage does not affect past political instability.13

IV. Empirical Results

The first objective of our empirical analysis is to identify the main political, institutional, and economic determinants of seigniorage levels across countries and time. Then, after finding strong support for our hypothesis that greater political instability leads to higher seigniorage, we try to determine under which circumstances or country characteristics that relationship is stronger. Finally, we perform a sensitivity analysis which checks whether or not the main results hold when an alternative definition of seigniorage is used, when the sample only includes developing countries, and when our main proxy for political instability is defined in a different way.

A. Main Determinants of Seigniorage Levels

The estimation results of the model described in the previous section, using a fixed effects specification,14 are shown in Table 3. The dependent variable is the change in reserve money as a percentage of GDP. All explanatory variables described in the previous section were included in the estimation reported in column 1. Since the Index of Economic Freedom is highly correlated with real GDP per capita and its Area III—Freedom to exchange with foreigners—already represents openness to trade, the variables real GDP per capita and trade (in percent of GDP) were not included in the model of column 2. Then, in column 3, the five component areas of the Index of Economic Freedom are included, so that we can determine which have greater effects on seigniorage.

Table 3.

Results for Seigniorage

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Panel regressions controlling for fixed effects. Seigniorage, the dependent variable, was defined as the ratio of the change in reserve money (IFS, line 14) to nominal GDP (IFS line 99b). Models estimated with a constant. T-statistics are in parentheses. Significance level at which the null hypothesis is rejected—*** 1 percent; ** 5 percent, and * 10 percent.

The results reported in the first three columns of Table 3 confirm the hypothesis that greater political instability leads to higher seigniorage levels and show that the effects are sizable— an additional cabinet change increases seigniorage by around 0.24 (an increase of 13 percent relative to the sample mean of 1.87). Economic freedom also has important effects on inflation; a move of one point up the scale (towards greater freedom) reduces seigniorage by roughly 1.2 (a decrease of 64 percent relative to the sample mean). Of its five component areas, only Area III (access to sound money) and Area IV (freedom to exchange with foreigners) are statistically significant, with a negative sign. Democracy does not seem to affect seigniorage levels, as Polity Scale is never statistically significant. Concerning the economic variables, only growth of real GDP per capita has statistically significant negative effects on seigniorage, as expected.15

The Index of Economic Freedom, for which data are available only after 1970, was not included in the last three estimations reported in Table 3. Its exclusion allows for the reintroduction of trade (in percent of GDP) and real GDP per capita in the model and causes several changes in results: the estimated coefficient and the degree of statistical significance of cabinet changes increases; agriculture (in percent of GDP) becomes highly statistically significant, real GDP per capita, although close to zero, becomes highly significant and changes sign relative to column 1; growth of real GDP per capita exhibits lower significance levels; and the ten-year period dummies are highly statistically significant. Although trade (in percent of GDP) has a positive sign and is statistically significant in the estimation of column 4, it is not significant when the alternative definition of seigniorage is used (result not reported). In column 5, the interaction variable external trade shocks, which is the product of trade (in percent of GDP) and percent change in terms of trade, is used instead of those two variables. Since it is not statistically significant, it is not included in the model of column 6, which is the reference for the models of the following tables. The positive and significant coefficients of the ten-year dummies indicate that seigniorage levels were higher in the 1980s, followed by the 1990s and the 1970s. The lowest levels of seigniorage were obtained in the 1960s, whose dummy variable was left out of the models.16

Results regarding political instability and economic freedom conform to our expectations and are consistent with those found by Aisen and Veiga (forthcoming) for inflation levels, and with the positive relationship between seigniorage and political instability identified by Cukierman, Edwards, and Tabellini (1992) using cross sectional data. Those concerning economic variables are consistent with the findings of previous studies, such as Chelliah, Baas, and Kelly (1975), Edwards and Tabellini (1992), and Click (1998), indicating that larger agricultural sectors and lower GDP per capita levels are associated with greater reliance on seigniorage revenues. Our expectation that lower rates of GDP growth reduce seigniorage also receives empirical support.

The results of a series of robustness tests, based on the model of column 6 of Table 3, are shown in Table 4. In columns 1 and 2, the Freedom House ratings of political rights and civil liberties, respectively, are used instead of the polity scale. Both have positive signs, indicating that higher values, associated with less rights and liberties, lead to higher seigniorage, but only civil liberties is marginally statistically significant. Since this result does not hold when we use the alternative definition of seigniorage, there is no robust evidence that democracy affects seigniorage levels.17

Table 4.

Robustness Tests

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Panel regressions with fixed effects. T-statistics are in parentheses. Significance level at which the null hypothesis is rejected—*** 1 percent, ** 5 percent, and * 10 percent. Seigniorage, the dependent variable, was defined as the ratio of the change in reserve money (IFS, line 14a) to total nominal GDP (IFS, line 99b). Models estimated with a constant and three decade dummies (1970s, 1980s, and 1990s). Their estimated coefficients are not shown in order to economize space.

In column 3, ideological polarization (DPI) is included in the base model. Although it has a positive sign, as expected, it is not statistically significant. The ideological orientation of the executive (higher values stand for more leftist governments) enters the model of column 4. Results indicate that more leftist executives are associated with higher seigniorage levels. This is consistent with Hibb’s (1977) hypothesis that left-wing oriented governments are relatively less concerned with inflation than right-wing ones. Results shown in columns 5 to 7 indicate that urbanization, trading partners’ GDP growth, and external debt do not affect seigniorage in a statistically significant way.18 Those of columns 8 and 9 are consistent with our expectation that more currency inside banks and exchange rate regimes closer to fixed exchange rates19 lead to lower seigniorage levels.20 Finally, the results of column 10 confirm Click’s (1998) result that seigniorage will be higher when the international creditworthiness of the country is lower.21 That is, when external borrowing is less available (or costlier), the government has to rely more heavily on seigniorage revenues.

B. Circumstances Under Which the Effects of Political Instability on Seigniorage Are Stronger

Although our results regarding the relationship between political instability and seigniorage are quite robust, it is possible that they are stronger in some circumstances or in countries with specific characteristics. Aisen and Veiga (forthcoming) found that political instability affected inflation levels especially in high-inflation and developing countries, whereas that relationship was practically nonexistent in low inflation and industrialized countries. In order to check if the same happens with seigniorage, we performed estimations in which cabinet changes was interacted with dummy variables accounting for annual inflation rates above and below 50 percent and for developing and industrial countries. The results shown in columns 1 and 2 of Table 5 are consistent with the results of Aisen and Veiga (forthcoming). That is, greater political instability, expressed in a larger number of cabinet changes, leads to higher seigniorage levels only in high-inflation and developing countries.

Table 5.

Results for Interactions of Cabinet Changes 1/

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Panel regressions controlling for fixed effects. Seigniorage, the dependent variable, was defined as the ratio of the change in reserve money (IFS, line 14) to total nominal GDP (IFS, line 99b). Models estimated with a constant and three decade dummies (1970s, 1980s, and 1990s). Their estimated coefficients are not shown in order to economize space. T-statistics are in parentheses. Significance level at which the null hypothesis is rejected—*** 1 percent; ** 5 percent, and * 10 percent.

According to Woo (2003), social polarization, which can be proxied by income inequality and the quality of institutions are important determinants of budget deficits. In highly polarized societies (where there is high income inequality), there is a high polarization of preferences among political parties and interest groups for different types of government spending. Then, according to the model of Cukierman, Edwards, and Tabellini (1992), high polarization of interests will lead to higher seigniorage, in the presence of high political instability. The quality of institutions is also very important because more stringent and transparent budgetary procedures, independence of the central bank, and greater parliamentary influence in the budgetary process can reduce the government’s ability to increase budget deficits and extract seigniorage revenues.

The hypothesis that the relationship between seigniorage and political instability is affected by social polarization (income inequality) is tested in column 3 of Table 5, where cabinet changes was interacted with dummy variables for average Gini coefficients above and below 40.22 Results suggest that political instability only leads to higher seigniorage in countries with large social polarization.23 The hypothesis that institutions affect that relationship was tested in columns 4 to 6, where cabinet changes was interacted with dummy variables for high and low turnover rates of central bank presidents,24 high and low economic freedom,25 and polity scale below and above zero. The results of column 3 imply that greater political instability will lead to higher seigniorage only when there is a high turnover rate of central bank presidents, that is, when the de facto independence of the central bank is low. When independence is high, seigniorage does not increase, as the government is no longer able to affect reserve money.26 Political instability also seems to affect seigniorage only in countries that have a low Index of Economic Freedom (column 5). This implies that the establishment of sounder and freer economic institutions is a way to avoid the above-referred relationship.27 More democratic institutions also seem to matter, as the results of column 6 indicate that democracies (polity scale>0) are associated with lower effects of political instability on seigniorage than authoritarian regimes (polity scale≤ 0).

Click (1998) showed that when governments face greater constraints to issue domestic and/or external debt, they will tend to resort more often to seigniorage revenues. We hypothesize that the effects of political instability on seigniorage levels also depend on the ratios of domestic debt to GDP and on the country’s creditworthiness. That is, when greater political instability leads to higher deficits, the government will resort more often to seigniorage revenues to finance them when domestic or foreign borrowing is more difficult (or costlier). The results of columns 3 and 4 provide empirical support for the above-referred hypothesis, as a greater number of cabinet changes is associated with higher seigniorage only in countries that have high domestic debt (column 3)28 or low creditworthiness (column 4).29

The effects of political instability on seigniorage were felt essentially during the 1970s and 1980s (see column 3), which is consistent with the fact that both political instability and seigniorage levels were higher in these decades. Columns 4 and 5 of Table 6 report the results of interacting cabinet changes with regional dummy variables. Those of column 1 indicate that the positive effect of political instability on seigniorage (defined as the ratio of the change in reserve money to GDP) is statistically significant only for Western Hemisphere (Latin American) countries. But, when the alternative definition of seigniorage (ratio of the change in reserve money to government revenues) is used, there are also significant effects for African countries (column 2).30

Table 6.

More Results for Interactions of Cabinet Changes 1/

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Panel regressions controlling for fixed effects. In columns 1 to 4, seigniorage, the dependent variable, was defined as the ratio of the change in reserve money (IFS, line 14) to total nominal GDP (IFS, line 99b). In column 5, seigniorage is the ratio of the change in reserve money (IFS, line 14) to government revenues (IFS, line 81). Models estimated with a constant and three decade dummies (1970s, 1980s, and 1990s). Their estimated coefficients are not shown in order to economize space. T-statistics are in parentheses. Significance level at which the null hypothesis is rejected—*** 1 percent, ** 5 percent, and * 10 percent.

C. Sensitivity Analysis

Table 7 shows the results of the interactions of alternative proxies of political instability with annual inflation rates above or below 50 percent. These proxies for political instability are defined as:

  • Government crises (CNTS)—counts the number of rapidly developing situations in a year that threaten to bring the downfall of the present regime.

  • Executive changes (CNTS)—counts the number of times in a year that effective control of the executive power changes hands.

  • Index of political cohesion (DPI)— 0 to 3 index based on Roubini and Sachs (1989) in which greater values imply lower cohesion (coalition or minority governments).

Table 7.

Results for Interactions of Other Proxies of Political Instability 1/

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Panel regressions controlling for fixed effects. Seigniorage, the dependent variable, was defined as the ratio of the change in reserve money (IFS, line 14) to total nominal GDP (IFS, line 99b). Models estimated with a constant and three decade dummies (1970s, 1980s, and 1990s). Their estimated coefficients are not shown in order to economize space. T-statistics are in parentheses. Significance level at which the null hypothesis is rejected—*** 1 percent, ** 5 percent, and * 10 percent.

As happened in Column 1 of Table 5, only the interactions with inflation≥ 50 are statistically significant. Thus, these results are robust to the use of different proxies for political instability.

Columns 1 to 4 of Table 8 report the results obtained for the alternative definition of seigniorage—change in reserve money as a percentage of government revenues. In the models of columns 5 to 7 the sample contains only developing countries, and seigniorage is defined as in the previous tables. Finally, in the models of columns 8 to 11, a three-year moving average of cabinet changes was used instead of its annual values, in order to better capture eventual persistent situations of political instability. In all cases, results are very similar to those obtained in Tables 3 and 4, meaning that our conclusions regarding the effects of political and economic variables on seigniorage levels remained practically the same.

Table 8.

Sensitivity Analysis 1/

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Panel regressions with fixed effects. T-statistics are in parentheses. Significance level at which the null hypothesis is rejected—*** 1percent, ** 5 percent, and * 10 percent. Models estimated with a constant and three decade dummies (1970s, 1980s, and 1990s). Their estimated coefficients are not shown in order to economize space. The sample and the definition of seigniorage used dependent variable) are indicated in the first row.

V. Conclusions

The main purpose of this paper was to identify the major determinants of the cross-country and cross-time variability of seigniorage. Using a dataset covering about 100 countries, from 1960 to 1999 and applying standard panel data techniques, we found that greater political instability leads to higher seigniorage. This result confirms the findings of previous studies such as Cukierman, Edwards, and Tabellini (1992) and Click (1998).

Our major contribution to the literature is that in addition to the above-referred result, we succeeded in determining comprehensively the circumstances under which political instability has a greater impact on seigniorage, a topic that, in our opinion, is very important but received little attention in previous studies. Our results indicate that the effect of political instability on seigniorage is stronger in high-inflation, developing, highly indebted, less democratic, and socially polarized economies. Moreover, although this relationship is particularly strong in Latin America, it is not exclusive to this region. Finally, we also found that countries with high turnover rates of central bank presidents (with lower de facto central bank independence), lower levels of economic freedom, and poorer creditworthiness ratings, rely more on seigniorage to finance their deficits.

The results of this study have policy implications that greatly contribute to the policy debate in high-inflation (seigniorage) and politically unstable economies. Our results show that countries adopting policies that target greater economic freedom, institutional strengthening—such as new laws governing central bank independence—and reduced income inequality, limit the negative effect of political instability on seigniorage and thus improve their chances of successfully lowering their dependence on seigniorage revenues to finance their governments’ deficits. After some time, they should benefit from lower inflation and, consequently, higher growth and economic prosperity.

References

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