Inflation, Inflation Uncertainty, Political Instability, and Economic Growth

This paper uses a new data set that better measures inflation uncertainty to investigate the effects of inflation and inflation uncertainty on economic growth. In the cross section of forty-four countries that the new data allows, inflation uncertainty has a significant negative impact on economic growth. Uncertainty performs better than inflation crisis as an explanatory variable of economic growth. However, the improved measure does not overcome the multicollinearity problem between inflation and inflation uncertainty. More importantly, measures of political instability dominate inflation and inflation crisis, in the sense of a Davidson-MacKinnon test. It thus appears that in this data set political instability accounts for the negative effect of inflation on economic growth. The evidence against an independent effect from inflation uncertainty is relatively weak. Fischer (1993) and Barro (1995) presented evidence of a significant negative relationship between inflation and economic growth. However, the source of the negative relationship remains a subject of dispute. Fischer argues that inflation proxies for generally poor policy. Sala-i-Martin (1991) suggests that the role of inflation may be created by spurious correlation arising from an omitted variable. For example, high inflation uncertainty may be the cause of slow growth, but the high cross-country correlation between inflation and inflation uncertainty may cloud the true underlying relationship. More generally, he argues that inflation may proxy for government failures that are to blame for the sluggish growth. In this paper we investigate these conjectures about the nature of the effect of inflation on economic growth, and focus on the possibility that political instability lies behind the inflation-growth nexus. Political instability may directly disrupt production or may hamper growth by threatening property rights that are essential to material progress. Since Barro (1991), the number of revolutions and coups per year has been a standard variable in growth equations to capture this effect. More such disruptions tend to lower growth. Alesina, Ozler, Roubini, and Swagel (1992) and Mauro (1995) using different measures and methods also find that political instability retards economic growth. Cuikerman, Edwards, and Tabellini (1992) (hereafter CET) argue that politically unstable countries are prone to high inflation. In the CET model, a high probability of a political regime shift dissuades the incumbent government from investing in the infrastructure necessary to collect income taxes. Instead, the incumbents will rely on seigniorage. Davis and Kanago (1996) argue that in the CET setting a more unstable government causes greater inflation uncertainty. Cuikerman and Meltzer (1986) make a similar point from a different perspective. In their model, political instability refers to changes in the preferences of the Central Bank, and preferences that change more frequently cause both higher and more uncertain money growth. In short, political instability may generate both high and uncertain inflation. So, inflation uncertainty and inflation may be significant in growth regressions only because they proxy for political instability. Earlier studies of the effect of inflation uncertainty on economic growth used the sample standard deviation of inflation as the measure of inflation uncertainty. This measure implies that the expected rate of inflation over the period was constant and equal to the sample mean. This simplification was forced by the absence of survey data and the cost of constructing a regression based measure of expected inflation for each country; and amounts to replacing uncertainty with variability. We have compiled from the pages of Business Asia, Business Europe, and Business Latin America annual forecasts of inflation for a cross section of 44 countries which allow us to construct a standard deviation of inflation that is a measure of uncertainty. We use this measure below to study the effects of inflation and inflation uncertainty on growth. We find that when our measure of inflation uncertainty alone is added to a typical growth equation, it exerts a significant negative influence on growth. This is consistent with earlier studies that used variability. Also like earlier studies, the effect of inflation uncertainty is not robust. Once inflation is also included in the regression, inflation uncertainty no longer affects growth, but neither does inflation. Bruno and Easterly (1995) argue that times of inflation crisis, inflation episodes that equal or exceed 40% per year for two consecutive years, may be the cause of low growth rather than the inflation itself. In our sample, inflation uncertainty retains its explanatory power once a dummy variable that identifies inflation crisis countries is included in the regression, but the crisis dummy is no longer significant. So, our measure of inflation uncertainty contains more information than does inflation crisis. However, it does not overcome the collinearity problem with inflation. We then include both political instability and an inflation measure in a standard growth equation. In these equations inflation and inflation crisis lose their significance, while the instability measure maintains its significance. Instability also performs better than inflation uncertainty, but the evidence in favor of instability over inflation uncertainty is weaker than it is for the other two inflation variables. This holds for the political instability measure developed by Edwards and Tabellini (1991) and the recent measure used by Mauro (1995). The results are different for revolutions and coups. Revolutions and coups contain more information than inflation crisis, but the same information as inflation or inflation uncertainty. In the next section we describe the data. In Section 3 we first discuss the basic growth equation and then the results for each of the political variables. We compare the political measures in Section 4. We discuss some issues concerning our results in Section 5, and conclude in Section 6. 1 See,for example, Levine and Zervos (1993). 2